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## **Meet the editor**

Dr. Mayank G. Vats is currently providing his services as the senior specialist in the Department of Pulmonary Medicine and Allergy and Sleep Medicine, Rashid Hospital, Dubai. His main area of research and clinical interest includes asthma, respiratory allergy, COPD, and sleep medicine. Dr. Vats completed his postgraduate education (MD) in Pulmonary Medicine; then, he did

DNB in Respiratory Medicine and DNB in Critical Care medicine from the prestigious Escorts Heart Institute in New Delhi, India, and then served as a consultant pulmonologist, intensivist, and sleep physician at Escorts Heart Institute and Apollo Hospital, New Delhi, India, for more than 5 years. Previously, he provided his services as the specialist in the Department of Medicine and Chest Disease, Al Qassimi Hospital, Sharjah, UAE. Dr. Vats also got extensive training in sleep medicine by the American Academy of Sleep Medicine at LDS Hospital, Salt Lake City, Utah, USA, and at Cleveland Sleep Disorders Research Center. Dr. Vats got the membership of Royal College of Physicians and Surgeons of Glasgow, UK, and was elected as a fellow of the American College of Physicians (FACP) and fellow of the American College of Chest Physicians (FCCP). He is the European diplomat in respiratory medicine. Dr. Vats is the member in good standing of various medical professional societies, like ERS, ACCP, AASM, APSR, and ISCCM, to name a few. He has authored many peer-reviewed articles including original research, case reports, review articles, and chapters published in international books and journals and edited two books on respiratory medicine. He is presently also involved in many research projects, clinical trials, guideline projects in Gulf countries, and academic activities for the specialist doctors in UAE.

## Contents

#### **Preface XI**

## Preface

Sleep disorders are rampantly rising throughout the world and affect people of all ages, gen‐ der, and ethnicity. Inadequate sleep, poor in quantity and quality, and an excessive daytime sleepiness negatively affect daily activities of life. It's estimated that in the USA around 60– 70 million people suffer from sleep disorder, and in developing countries sleep disease af‐ fects more than 200 million people. Considering the significant magnitude of sleep disor‐ ders, there is an increasing interest in sleep medicine among physicians, researchers, and the general public.

Sleep medicine is a relatively new specialty, still developing, although sleep has remained an important area of curiosity since the human civilization with emphasis on the physiolog‐ ic and psychological basis and significance of sleep by many famous scientists.

Since then and especially in the past few years, sleep medicine is developing rapidly with more than 100 sleep disorders discovered till now. Despite that, sleep specialty is in neonatal stage especially in developing and underdeveloped countries. Sleep medicine is still evolv‐ ing with an ongoing worldwide clinical research, training programs, and changes in the in‐ surance policy disseminating more awareness in physicians and patients.

Sleep apnea is one of the most common sleep disorders, found in around 5–7% of the gener‐ al population with high prevalence in the obese, elderly individuals but largely unrecog‐ nized and hence undiagnosed with untreated and life-threatening consequences.

In the last decade, new complex sleep disorders and its pathophysiology have been discov‐ ered, new treatments options (pharmacological and nonpharmacological) are available, and hence we planned a book on the recent developments on one of the most common sleep disorders, sleep apnea.

We have incorporated chapters from the eminent clinicians and authors around the globe to produce a state-of-the-art book with the target audience from internal medicine, pulmonary, sleep medicine, neurology, ENT, and psychiatry discipline.

With the blessings of the almighty God and my parents, this important task has been com‐ pleted in a successful way.

I would like to convey special thanks to my wife Dr. Deepa Vats, who has special interest in pediatric sleep medicine, and my daughters Spraha Vats and Aadhya Vats for their constant and untiring support and encouragement.

> **Dr. Mayank G. Vats** Senior Specialist, Pulmonologist, Intensivist, and Sleep Physician Rashid Hospital and Dubai Hospital, Dubai, UAE

#### **Chapter 1 Provisional chapter**

#### **Sleep Apnea – Recent Updates Sleep Apnea – Recent Updates**

Samson Z. Assefa, Montserrat Diaz-Abad and Steven M. Scharf Samson Z. Assefa, Montserrat Diaz-Abad and Steven M. Scharf

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66816

#### **Abstract**

Sleep apnea is highly prevalent and underdiagnosed. It is associated with multiple medical conditions including cardiac dysrhythmia, stroke, hypertension, diabetes and congestive heart failure. In the last few decades, advances in diagnosis and treatment of sleep apnea have been robust. In this review, we will emphasize primarily developments in the area of sleep apnea that occurred in the past 5 years. These include changes in the nomenclature of sleep apnea in the International Classification in Sleep Disorders (ICSD)-3, physiologic approach of treating sleep apnea, eligibility for CPAP (continuous positive airway pressure) treatment, home sleep testing (HST), sleep apnea in pregnancy, updates in oral device treatment and other emerging concepts on sleep apnea.

**Keywords:** sleep apnea, sleep apnea updates, obstructive sleep apnea, apnea, recent updates of sleep apnea

#### **1. Introduction**

Obstructive sleep apnea (OSA) is a prevalent condition associated with increased risk of developing hypertension, heart failure, type 2 diabetes, cardiac rhythm disturbances, stroke and increased all-cause mortality [1–7]. It is also associated with reduced quality of life and sleepiness. In the field of sleep disorders particularly diagnosis and treatment of sleep apnea continues to evolve. In this review article, we consider advances in our understanding of pathophysiology, diagnosis and treatment sleep apnea with emphasis on recent advances over the past 5 years.

Sleep disordered breathing (SDB) events are classified as *obstructive*, *central* or *mixed*. Furthermore, events are usually subdivided into *apneas* (complete or almost complete cessation of airflow), *hypopneas* (reduction of airflow by 30-90% associated with EEG

arousals and/or 3-4 % oxygen desaturation) and *respiratory event-related arousals* (RERA reduction of airflow by <30% associated with flow limitation and arousals on EEG) [8]. Obstructive events occur as a result of partial or complete obstruction of the upper airway at the level of the oro and/or nasopharynx with continuing respiratory efforts. Central events occur as a result of partial or complete cessation of efferent respiratory signals from the brainstem. Mixed events start out as central and evolve into obstructive events. Disordered breathing events (DBE) may be associated with reductions in oxygen saturation, sympathetic and parasympathetic surges and, in the case of obstructive events, large swings in intrathoracic pressure. Treatments for obstructive sleep apnea have classically included CPAP, certain types of upper airway surgery, dental orthotic or mandibular advancement devices, weight loss and positional therapy [9].

In the ensuing review, we discuss recent advances in the field including means of diagnosis and treatment in light of currently available literature.

#### **2. Sleep-related breathing disorders nomenclature in ICSD-3**

In the year of 2014, the American Academy of Sleep Medicine (AASM) released the 3rd edition of International Classification in Sleep Disorders (ICSD) [10]; this was an upgrade to the ICSD-2, 2011 edition. This shows progressive evolution of the nosology as knowledge and literature related to sleep disorders become more robust. There is a significant content change in the new edition, and one of them is within the sleep-related breathing disorders section. Treatment-central sleep apnea now appears as an isolated term to be used to describe central sleep apnea in the context of positive airway pressure treatment for obstructive sleep apnea. Other central sleep apneas like Cheyne-Stokes and substance induced are not classified in the same category. The other change in the SDB section is a separate diagnosis called sleeprelated hypoxemia. This was under the same category of hypoventilation in the previous edition. In the ICSD-2, different categories based on the causes of the hypoxemia/hypoventilation that include medical and neurologic were listed separately. In ICSD-3, the cause of the hypoxemia/hypoventilation has to be diagnosed separately. Sleep-related hypoxemia diagnosis is assigned if a sleep study showed a sustained drop in SaO2 but normal or not measured PaCO2 . In ICSD-3, obesity hypoventilation is also listed as a separate disorder due to its distinct clinical behavior. This requires a documentation of awake PaCO2 >45 mm Hg. Refer to ICSD-3 for detailed review of the changes in all other sections [10].

#### **3. Home sleep testing (HST)**

OSA is prevalent and carries numerous physiologic and clinical consequences. The most recent prevalence estimates are that OSA is found in 33.9% of men and 17.4% of women [11]. These estimates are greater than previous ones, possibly due to increased sensitivity of detection, changes in definitions of types of "events," and/or increasing rates of obesity [11, 12]. Furthermore, as untreated sleep apnea is associated with a range of adverse consequences [13], it has become clear that diagnostic testing needs to be convenient and available. Traditionally, in-laboratory attended polysomnography (PSG), in which sleep staging and cardiorespiratory variables are continuously recorded, has been the preferred method for diagnosing OSA. However, relatively high cost and growing wait times have provided the impetus for simplified portable unattended systems suitable for diagnosis of OSA outside the laboratory environment. In 1994 [14], AASM published a classification scheme grading the complexity of diagnostic sleep testing (see **Table 1**). Under this system, level 1 refers to commonly used in-laboratory attended PSG, level 2 refers to equally complex attended studies at home (rarely done) and levels 3 and 4 refer to unattended studies most commonly done at home, or out of the sleep laboratory. Since the original AASM classification system was published, technological advances have led to the availability of portable monitoring devices that may not neatly fit into the classification scheme. A revised system was presented in 2011 [15] similar to the 1994 system, but categorized portable devices according to the type of recording channel and the technology utilized. One of the primary issues is whether or not the device can adequately differentiate sleep from wakefulness and even stages of sleep. Many portable monitoring systems do not directly measure sleep using an EEG, but rather use derivative signals such as movement (actigraphy), pulse wave coupling or other derivative signals. For many systems, disease severity is more appropriately expressed as "respiratory disturbance index (RDI)" or "respiratory event index (REI)" (number of apneas/hypopneas per hour in bed) rather than the traditional apnea hypopnea index (apneas/hypopneas per hour of sleep) (AHI).

**Table 1.** Levels of sleep testing (4).

arousals and/or 3-4 % oxygen desaturation) and *respiratory event-related arousals* (RERA reduction of airflow by <30% associated with flow limitation and arousals on EEG) [8]. Obstructive events occur as a result of partial or complete obstruction of the upper airway at the level of the oro and/or nasopharynx with continuing respiratory efforts. Central events occur as a result of partial or complete cessation of efferent respiratory signals from the brainstem. Mixed events start out as central and evolve into obstructive events. Disordered breathing events (DBE) may be associated with reductions in oxygen saturation, sympathetic and parasympathetic surges and, in the case of obstructive events, large swings in intrathoracic pressure. Treatments for obstructive sleep apnea have classically included CPAP, certain types of upper airway surgery, dental orthotic or mandibular

In the ensuing review, we discuss recent advances in the field including means of diagnosis

In the year of 2014, the American Academy of Sleep Medicine (AASM) released the 3rd edition of International Classification in Sleep Disorders (ICSD) [10]; this was an upgrade to the ICSD-2, 2011 edition. This shows progressive evolution of the nosology as knowledge and literature related to sleep disorders become more robust. There is a significant content change in the new edition, and one of them is within the sleep-related breathing disorders section. Treatment-central sleep apnea now appears as an isolated term to be used to describe central sleep apnea in the context of positive airway pressure treatment for obstructive sleep apnea. Other central sleep apneas like Cheyne-Stokes and substance induced are not classified in the same category. The other change in the SDB section is a separate diagnosis called sleeprelated hypoxemia. This was under the same category of hypoventilation in the previous edition. In the ICSD-2, different categories based on the causes of the hypoxemia/hypoventilation that include medical and neurologic were listed separately. In ICSD-3, the cause of the hypoxemia/hypoventilation has to be diagnosed separately. Sleep-related hypoxemia diagnosis is

In ICSD-3, obesity hypoventilation is also listed as a separate disorder due to its distinct clini-

OSA is prevalent and carries numerous physiologic and clinical consequences. The most recent prevalence estimates are that OSA is found in 33.9% of men and 17.4% of women [11]. These estimates are greater than previous ones, possibly due to increased sensitivity of detection, changes in definitions of types of "events," and/or increasing rates of obesity [11, 12]. Furthermore, as untreated sleep apnea is associated with a range of adverse consequences

but normal or not measured PaCO2

>45 mm Hg. Refer to ICSD-3 for

.

advancement devices, weight loss and positional therapy [9].

**2. Sleep-related breathing disorders nomenclature in ICSD-3**

and treatment in light of currently available literature.

2 Sleep Apnea - Recent Updates

assigned if a sleep study showed a sustained drop in SaO2

detailed review of the changes in all other sections [10].

**3. Home sleep testing (HST)**

cal behavior. This requires a documentation of awake PaCO2

A complete review of all home testing systems available is beyond the scope of this review. However, **Table 2** presents examples of several of the available simpler systems that have been validated. Both the AASM and the Canadian Sleep Society have published guidelines for use of portable monitors, most recently in 2010 [10]. These guidelines generally follow the highly selective study criteria outlined in validation studies. Portable monitoring devices are appropriate for patients with a high pretest probability of moderate-to-severe OSA (AHI of 15 or greater), but are not appropriate for routine screening in asymptomatic patients, or patients with concomitant medical or sleep disorders, such as central sleep apnea or periodic limb movement disorder. The Centers for Medicare and Medicaid Services (CMS) has approved coverage for PAP devices for patients diagnosed with OSA using portable monitoring [11].

**Table 2.** Examples of portable home testing equipment (validation studies).

Many third party payers have followed CMS' lead; and in fact, many have instituted policies whereby portable sleep testing is required for all covered patients, with certain exceptions. Indeed in the highly selected patient populations studied for validation of portable testing, the correlation and even clinical outcomes are comparable between using portable diagnostic and in-laboratory testing [12–14]. However, as pointed out in an editorial by Collop [15], the issue is not the test *per se*, but how the test is utilized when it is "generalized." Most home sleep testing studies are done with highly selected patients (for the study quoted in Ref. [16], 272 patients were highly screened, 102 were randomized, approximately half to home testing). Furthermore, patients were evaluated by sleep experts, and scoring was done by well-trained and motivated technologists. Exclusions for significant medical, psychiatric and sleep disorders were rigidly carried out. However, in the "real world," as insurance carriers try to minimize costs, the experience is often that these conditions are not met. The decision to accept and indeed to "push for" home testing is often made on the basis of business and finance rather than patient benefit. While home or out of center sleep testing offers a number of advantages compared with in-laboratory PSG, there is no evidence that using this approach for all or the majority of patients is advantageous, even financially. The initial costs are generally less than those of in-laboratory testing. Furthermore, home testing offers a more rapid method of assessing the many patients with undiagnosed OSA who have limited access to, or who are reluctant to undergo, inlaboratory PSG. However, Chervin et al. [17] performed a careful cost utility analysis, comparing in-laboratory PSG, out of center testing and no testing (with treatment based on clinical characteristics). Their outcomes were based on costs per quality-adjusted life years over 5 years. These authors concluded that standard in-lab PSG provides greater qualityadjusted life years over 5 years than either out of center testing or no testing. Reuveni et al. [18] modeled costs of in-laboratory PSG versus out of center testing, accounting for the published technical failure rate of out of center testing, and the published European costs for PSG. They demonstrated that there was no long-term cost saving using out of center testing versus in-laboratory PSG.

Given that sleep apnea is under-diagnosed, another advantage of HST is diagnosing patients in a hospital setting and arranging follow-ups for complete evaluation in out-patient settings. Kauta et al. [19] evaluated 104 cardiac patients with SDB symptoms who are hospitalized for heart failure, arrhythmia and myocardial infarction. They performed type III portable sleep study, and 78% had SDB (AHI >5 events/h). Patients diagnosed with SDB were started with PAP treatment. At 30 days, adherence to PAP and 30-day readmission rate were assessed. None (0%) of patients (0/19) with adequate adherence, 30% of patients with partial adherence (6/20) and 29% of nonusers (5/17) were readmitted or visited emergency room for cardiac issues (p = 0.025).

#### **4. Effects of different definitions of DBEs on CPAP eligibility**

Many third party payers have followed CMS' lead; and in fact, many have instituted policies whereby portable sleep testing is required for all covered patients, with certain exceptions. Indeed in the highly selected patient populations studied for validation of portable testing, the correlation and even clinical outcomes are comparable between using portable diagnostic and in-laboratory testing [12–14]. However, as pointed out in an editorial by Collop [15], the issue is not the test *per se*, but how the test is utilized when it is "generalized." Most home sleep testing studies are done with highly selected patients (for the study quoted in Ref. [16], 272 patients were highly screened, 102 were randomized, approximately half to home testing). Furthermore, patients were evaluated by sleep experts, and scoring was done by well-trained and motivated technologists. Exclusions for significant medical, psychiatric and sleep disorders were rigidly carried out. However, in the "real world," as insurance carriers try to minimize costs, the experience is often that these conditions are not met. The decision to accept and indeed to "push for" home testing is often made on the basis of business and finance rather than patient benefit. While home or out of center sleep testing offers a number of advantages compared with in-laboratory PSG, there is no evidence that using this approach for all or the majority of patients is advantageous, even financially. The initial costs are generally less than those of in-laboratory testing. Furthermore, home testing offers a more rapid method of assessing the many patients with undiagnosed OSA who have limited access to, or who are reluctant to undergo, inlaboratory PSG. However, Chervin et al. [17] performed a careful cost utility analysis, comparing in-laboratory PSG, out of center testing and no testing (with treatment based on clinical characteristics). Their outcomes were based on costs per quality-adjusted life years over 5 years. These authors concluded that standard in-lab PSG provides greater qualityadjusted life years over 5 years than either out of center testing or no testing. Reuveni et al. [18] modeled costs of in-laboratory PSG versus out of center testing, accounting for the published technical failure rate of out of center testing, and the published European costs for PSG. They demonstrated that there was no long-term cost saving using out of center

**System Level Principles/comments References**

3\* Measures O2

**Table 2.** Examples of portable home testing equipment (validation studies).

ApneaStripR: S.L.P. Ltd 3\* Simple device records airflow overnight and estimates sleep time

from forehead vein, measures O2

3 Directly measures airflow, estimates respiratory effort

3\* Uses proprietary algorithm combined peripheral arterial tonometry, oximetry, heart rate, actigraphy to estimate sleep time and calculate respiratory disturbance index

Approximates sleep time using lack of head movement

from optical volumetric signals. Proprietary algorithm detects sleep, respiration and disordered breathing events

saturation, pulse, peripheral arterial tone

saturation and pulse rate.

[6]

[7]

[8]

[9]

testing versus in-laboratory PSG.

Apnea risk evaluation system (ARES)R: SleepMed, Inc

4 Sleep Apnea - Recent Updates

WatchPatR device: Itamar

PhotoplethysmographR: MorpheusOx; Widemed ltd.

\*Level claimed by manufacturer.

Medical Ltd

Treatment with CPAP is known to significantly reduce the risk of important cardiovascular events and overall health care utilization [20, 21]. Thus, diagnosis and treatment of OSA would be expected to have a considerable beneficial impact on public health. Eligibility for CPAP treatment is usually based on disease severity, and this is usually expressed as the AHI. Of course, the number of events must perforce be based on the specific definitions of apneas, hypopneas and specified comorbid conditions. In 2012, the AASM adopted modified definitions of DBEs [8]. However, some insurance carriers including CMS continued to use the 2007 AASM definitions of DBEs [22]. The definitions of apneas between 2007 and 2012 have not changed [23], that is, a >90% reduction in airflow with continuing respiratory effort (for obstructive events). However, the 2012 AASM definition of hypopneas [8] calls for a 30–90% reduction in airflow associated with either a 3% reduction in O2 saturation or a terminal arousal. The current CMS definition of hypopneas calls for the same reduction in airflow, but associated with a 4% reduction in O2 saturation [23]. Further CMS defines the eligibility for CPAP treatment based on the AHI as follows: Patients are eligible for CPAP treatment for AHI ≥ 15, or if AHI is 5–14, only if the patient has a specified comorbidity, including hypertension, excessive sleepiness, impaired cognition, mood disorder, insomnia, ischemic heart disease, and history of stroke. Since CMS is often used by other insurance carriers as a model for designing their own treatment criteria, the differences in hypopnea definitions or in designation of treatment eligibility could have real significance.

Ho et al. [24] recently reviewed data on 6441 patients from the sleep heart health study and found, not surprisingly, that there was a discrepancy in the AHI depending on the definitions used for hypopneas, the discrepancy being greater at low AHIs than at high ones. Korotinsky et al. [25] recently compared AHI's calculated using both AASM (2012) and CMS definitions of hypopneas, as well as eligibility for CPAP treatment in a convenience sample of 112 consecutive patients studied in their sleep laboratory. Eighty-five patients were <65 years old and 27 were >65 years old (eligible for Medicare). They found the largest discrepancies in the younger patients, but a nonstatistically significant difference in the older patients. Furthermore, because of the presence of comorbidities in the older patients, there were no differences in eligibility for CPAP no matter which set of criteria were used. Thus, in younger patients, application of the stricter CMS criteria would have resulted in fewer patients being eligible for CPAP treatment, but not in the older patients. Thus, in the younger patients, application of the stricter CMS criteria for eligibility for CPAP treatment would have resulted in fewer patients with relatively mild (AHI, 5–14) OSA being treated with CPAP. The question as to whether there is a healthcare advantage for treatment of younger patients with mild disease is still unsettled with opinions on both sides of the question [25].

#### **5. Toward a physiologic approach to treating OSA**

Breathing involves a complex neurologic interaction of various types of inspiratory muscles. During inspiration, pressures down the airway are slightly negative, since air must move from atmospheric pressure ( = 0) to alveoli (pressure slightly negative). Prior to activation of the diaphragm, there is the activation of upper airway/pharyngeal dilator muscles that prevent collapse of the upper airway during inspiration. Thus, the upper airway performs an important function during respiration, and if function is compromised, obstruction could result as in OSA [26, 27]. CPAP is one of the preferred treatments for moderate-to-severe OSA. However, since upper airway dilator stimulation is thought to be inadequate to maintain upper airway patency during sleep, especially during REM sleep when skeletal muscle tone is suppressed, the concept developed that electrical stimulation of upper airway dilator muscles during inspiration could help maintain airway patency. Thus, a number of systems have been developed whereby stimulation of a hypoglossal nerve through an implantable device, timed to the patient's normal inspiration could help to maintain airways patency and alleviate sleep apnea. This approach would be particularly useful in patients who cannot tolerate or refuse to tolerate CPAP or other treatments. Several clinical trials have been carried out on devices implanted subcutaneously that are, once activated, triggered by the patient's own inspiratory effort [27–33]. The largest of these [33], a multisite clinical trial of patients with moderate-to-severe OSA, surgically implanted a hypoglossal nerve stimulator in OSA patients who were CPAP intolerant or refused CPAP treatment. The primary outcome measures were AHI and the ODI4 (oxygen desaturation index—number of times per hour, O2 saturation fell by at least 4%). Secondary outcomes included the Epworth Sleepiness Scale (ESS), the Functional Outcomes of Sleep Questionnaire and the percent of sleep time with oxygen saturation <90%. This single cohort included 126 patients. At the end of 1 year, the median AHI decreased from 29.3 per hour to 9.0 per hour with similar improvements in the ODI4. Quality of life (QOL) measures also improved at the end of 1 year. At the end of 1 year, 46 patients participated in a 1:1 randomized therapy withdrawal trial. In this phase, participants who had therapy withdrawn demonstrated return of disease severity compared with those in whom therapy was not withdrawn. **Table 3** lists appropriate criteria for therapy with a hypoglossal stimulator. Finally, it should be pointed out that implantation of a hypoglossal nerve stimulator is part of a comprehensive program that extends well beyond the surgical procedure. The complete details are beyond the scope of this review, but involve selection based on criteria presented in **Table 3**, endoscopic evaluation of pharyngeal collapse, training of patients and staff, and various stages of activation and titration of stimulation parameters.

**Table 3.** Criteria for consideration for hypoglossal nerve implantation.

#### **6. Sleep apnea and telemedicine**

younger patients, application of the stricter CMS criteria for eligibility for CPAP treatment would have resulted in fewer patients with relatively mild (AHI, 5–14) OSA being treated with CPAP. The question as to whether there is a healthcare advantage for treatment of younger patients with mild disease is still unsettled with opinions on both sides of the

Breathing involves a complex neurologic interaction of various types of inspiratory muscles. During inspiration, pressures down the airway are slightly negative, since air must move from atmospheric pressure ( = 0) to alveoli (pressure slightly negative). Prior to activation of the diaphragm, there is the activation of upper airway/pharyngeal dilator muscles that prevent collapse of the upper airway during inspiration. Thus, the upper airway performs an important function during respiration, and if function is compromised, obstruction could result as in OSA [26, 27]. CPAP is one of the preferred treatments for moderate-to-severe OSA. However, since upper airway dilator stimulation is thought to be inadequate to maintain upper airway patency during sleep, especially during REM sleep when skeletal muscle tone is suppressed, the concept developed that electrical stimulation of upper airway dilator muscles during inspiration could help maintain airway patency. Thus, a number of systems have been developed whereby stimulation of a hypoglossal nerve through an implantable device, timed to the patient's normal inspiration could help to maintain airways patency and alleviate sleep apnea. This approach would be particularly useful in patients who cannot tolerate or refuse to tolerate CPAP or other treatments. Several clinical trials have been carried out on devices implanted subcutaneously that are, once activated, triggered by the patient's own inspiratory effort [27–33]. The largest of these [33], a multisite clinical trial of patients with moderate-to-severe OSA, surgically implanted a hypoglossal nerve stimulator in OSA patients who were CPAP intolerant or refused CPAP treatment. The primary outcome measures were AHI and the ODI4 (oxygen desaturation index—number of times per

saturation fell by at least 4%). Secondary outcomes included the Epworth Sleepiness

Scale (ESS), the Functional Outcomes of Sleep Questionnaire and the percent of sleep time with oxygen saturation <90%. This single cohort included 126 patients. At the end of 1 year, the median AHI decreased from 29.3 per hour to 9.0 per hour with similar improvements in the ODI4. Quality of life (QOL) measures also improved at the end of 1 year. At the end of 1 year, 46 patients participated in a 1:1 randomized therapy withdrawal trial. In this phase, participants who had therapy withdrawn demonstrated return of disease severity compared with those in whom therapy was not withdrawn. **Table 3** lists appropriate criteria for therapy with a hypoglossal stimulator. Finally, it should be pointed out that implantation of a hypoglossal nerve stimulator is part of a comprehensive program that extends well beyond the surgical procedure. The complete details are beyond the scope of this review, but involve selection based on criteria presented in **Table 3**, endoscopic evaluation of pharyngeal collapse, training of patients and staff, and various stages of activation and titration of stimula-

**5. Toward a physiologic approach to treating OSA**

question [25].

6 Sleep Apnea - Recent Updates

hour, O2

tion parameters.

As compared to the past few decades, there is a better sleep disorders recognition and understanding of the impacts. As a result, a number of patients who need integrated expertise of sleep medicine care have increased. However, there is a substantial shortage of sleep medicine specialists across the United States. AASM recognized telemedicine could be used as a tool to improve the specialist gap and deliver a cost-effective care while still maintaining high-quality care. The The American Telemedicine Association defines telemedicine as the use of medical information exchanged from one site to another via electronic communications to improve a patient's clinical health status. This includes e-mail, smart phones, wireless tools, two-way video and other forms of telecommunications technology [34].

Telemedicine has been implemented in the majority of the medical disciplines. In the early days, telemedicine was used in the field of sleep medicine mainly to promote and reinforce CPAP therapy adherence and showed mixed results. In other instances, transmission of sleep studies by non-specialist to a sleep specialist for review has been used [35]. Taylor et al. [36] randomly grouped patients to usual care and telemedicine-based adherence for CPAP. Usual care patients visited practitioners in clinics and patients on telemedicine group had computer-based monitoring device that did not include video conferencing. Participants are contacted either by phone or by computer-based system. The study found no significant difference between the two groups. Other limited telemedicine application was doing sleep studies and diagnosing sleep disorders. Mendelson et al. [37] in 2014 randomized a total of 107 hypertensive patients to CPAP care (n = 53) and CPAP care with a telemedicine intervention (n = 54). Patients assigned to telemedicine uploaded blood pressure (BP) measurement, CPAP adherence, sleepiness and quality of life data and in return on regular bases they received recommendations. The main outcome was home self-measurement of BP improvement. Telemedicine-supported CPAP users did not improve BP and cardiovascular risk in high-risk OSA patients.

In 2008, the Milwaukee Veterans Administration Medical Center evaluated the application of telemedicine in sleep medicine [38]. Based on electronic consult eligibility for portable study, patients were assessed and sleep study orders were placed by sleep specialists. The need for inperson assessment was also evaluated, and appointments were scheduled. CPAP was ordered for confirmed sleep apnea. Baig et al. [38] retrospectively assessed the 5-year trend in accessibility to and receipt of care after the program was implemented. They found that, in spite of increased volume of services, the interval between sleep consult and PAP prescription decreased from >60 days to <7 days. However, there was no change in clinic wait time of >60 days.

In the past decade, the use of tele-sleep medicine has been expanded to include patient's sleep evaluation. Before AASM came up with recommendation on telemedicine for sleep medicine, there were studies that supported telemedicine could be used for complete evaluation of sleep medicine patients. A pilot study by Spaulding et al. [39] showed the application of telemedicine that included video conferencing. The group established tele-heath service in a rural area of Kansas after training nurses and Registered Polysomnography Technologists (RPSGT) on how to use the videoconferencing webcam and intraoral camera for examining severity of airway narrowing. There were 18 new patients visits and four follow-ups. They reported that telemedicine was effective for physician-patient interaction and visualizing the upper airway. The only problem was nurses had to be trained to present patients and use the video cam and oral camera.

The AASM published a position paper in 2015 [40] that telemedicine can be used to improve access to sleep medicine services provided by board-certified sleep medicine specialist and improve communications with other specialties. Telemedicine applications can be broadly categorized into two: synchronous and asynchronous interactions. Synchronous is a live, real-time, bidirectional, audio-video conferencing provider-patient interaction who are distant apart. Tele-stethoscope and mobile cameras can be used for physical exam that is done in the presence of a presenter who usually is a trained nurse practitioner, physician assistant, respiratory therapist, RPSGT or medical office assistant. The patient presenter gives a clinical support and assistance with physical exam. Asynchronous evaluation uses multiple models and involves the encounters occur at different times and are communicated one directionally between patients and providers electronically. AASM recommended providers to adopt technical requirements from the American Telemedicine Guidelines [40]. AASM believes if the technical, organizational and healthcare professional requirements are met, synchronous encounters could function as live office visits.

In January 2016, the AASM officially launched AASM SleepTM. This is a telemedicine platform designed for the sleep medicine field. Some centers have implemented telemedicine. Issues that need further clarification while implementing telemedicine include cost uncertainties, reimbursement structure and licensing rules. Currently, expansion of telemedicine to all sleep disorders has its own restrictions and providers should refer to their local standard for the technical and organizational requirements.

#### **7. Sleep apnea and pregnancy**

#### **7.1. Screening OSA during pregnancy**

In general, sleep disturbances are highly prevalent during pregnancy including SDB. Selfreported snoring is common with a prevalence of 14–41% as compared with 4–17% in nonpregnant women [41–46]. Recognition of sleep apnea in pregnant women in particular is difficult because pregnancy is dynamic process and multiple studies at different time points during pregnancy may not be feasible. In 2015, there were two articles that tried to assess the screening tools for sleep apnea in pregnancy. Lockhart et al. [47] assessed 218 third trimester pregnancies of which 12% had sleep apnea diagnosed using portable home sleep testing. In this study STOP, STOP BANG, Berlin, American Society of Anesthesiologist Checklist and ESS were not successful in detecting sleep apnea. However, some of the elements such as BMI, neck circumference, diagnosis of hypertension (HTN) and falling asleep while talking to others where more predictive based on univariate and multivariate analysis. Tantrakul et al. [48] evaluated Berlin and STOP BANG questionnaires to detect OSA across trimesters of high-risk pregnancy. They consisted of n = 72 (first trimester n = 23, second trimester n = 24 and third trimester n = 25), and with prevalence of OSA by trimester from first to third was 30.4%, 33.3% and 32.0%, respectively. Overall, predictive values of Berlin and STOP BANG were fair (AUC 0.72 for Berlin, P = 0.003, 0.75 for STOP BANG, P = 0.0001). The predictive values performed poorer during the first trimester. Multivariant analyses showed pre-pregnancy BMI, snore frequently and weight gain/BMI were significantly associated with OSA in first, second and third trimesters, respectively. Izci et al. [46] demonstrated third trimester pregnant females have smaller mean pharyngeal areas when compared with postpartum in supine, lateral and seated positions with a mean difference of 0.20 (95% CI 0.06–0.35), 0.26 (95% CI 0.12–0.39) and 0.18 (95% CI 0.02–0.32), respectively.

#### **7.2. OSA and perinatal outcomes**

person assessment was also evaluated, and appointments were scheduled. CPAP was ordered for confirmed sleep apnea. Baig et al. [38] retrospectively assessed the 5-year trend in accessibility to and receipt of care after the program was implemented. They found that, in spite of increased volume of services, the interval between sleep consult and PAP prescription decreased

In the past decade, the use of tele-sleep medicine has been expanded to include patient's sleep evaluation. Before AASM came up with recommendation on telemedicine for sleep medicine, there were studies that supported telemedicine could be used for complete evaluation of sleep medicine patients. A pilot study by Spaulding et al. [39] showed the application of telemedicine that included video conferencing. The group established tele-heath service in a rural area of Kansas after training nurses and Registered Polysomnography Technologists (RPSGT) on how to use the videoconferencing webcam and intraoral camera for examining severity of airway narrowing. There were 18 new patients visits and four follow-ups. They reported that telemedicine was effective for physician-patient interaction and visualizing the upper airway. The only problem was nurses had to be trained to present patients and use the

The AASM published a position paper in 2015 [40] that telemedicine can be used to improve access to sleep medicine services provided by board-certified sleep medicine specialist and improve communications with other specialties. Telemedicine applications can be broadly categorized into two: synchronous and asynchronous interactions. Synchronous is a live, real-time, bidirectional, audio-video conferencing provider-patient interaction who are distant apart. Tele-stethoscope and mobile cameras can be used for physical exam that is done in the presence of a presenter who usually is a trained nurse practitioner, physician assistant, respiratory therapist, RPSGT or medical office assistant. The patient presenter gives a clinical support and assistance with physical exam. Asynchronous evaluation uses multiple models and involves the encounters occur at different times and are communicated one directionally between patients and providers electronically. AASM recommended providers to adopt technical requirements from the American Telemedicine Guidelines [40]. AASM believes if the technical, organizational and healthcare professional requirements are met, synchronous

In January 2016, the AASM officially launched AASM SleepTM. This is a telemedicine platform designed for the sleep medicine field. Some centers have implemented telemedicine. Issues that need further clarification while implementing telemedicine include cost uncertainties, reimbursement structure and licensing rules. Currently, expansion of telemedicine to all sleep disorders has its own restrictions and providers should refer to their local standard for

In general, sleep disturbances are highly prevalent during pregnancy including SDB. Selfreported snoring is common with a prevalence of 14–41% as compared with 4–17% in non-

from >60 days to <7 days. However, there was no change in clinic wait time of >60 days.

video cam and oral camera.

8 Sleep Apnea - Recent Updates

encounters could function as live office visits.

the technical and organizational requirements.

**7. Sleep apnea and pregnancy**

**7.1. Screening OSA during pregnancy**

Repeated upper airway resistance and/or obstruction during sleep due to DBEs that causes chronic intermittent hypoxia, hypercarbia and sleep disruptions is believed to be a culprit for higher incidence of negative perinatal outcomes. However, studies have shown conflicting results. Tauman et al. [49] recruited 122 pregnant women with habitual snoring and 39% had SDB. In those pregnant women who snored had increased markers of fetal distress, which are circulating nucleated RBC, EPO and IL-6. However, there was no difference in neonatal outcome. Trudell et al. [50] conducted a study with the aim to develop a tool for airway assessment to predict adverse pregnancy outcomes. They hypothesized that higher Mallampati score (MS) is associated with adverse perinatal outcomes. Outcomes were compared between low MS and high MS in a total of 1823 term births. No significant difference was found in the risk of small for gestational age (SGA) [adjusted odds ratio 0.9 (95% CI 0.6–1.2), preeclampsia adjusted odds ratio 1.2 (95% CI 0.8–1.9) or neonatal acedemia 0.8 (95% CI 0.3–2)]. In recent population-based retrospective study (n = 636,227), Bin et al. [51] found that OSA was significantly associated with HTN, planned delivery, preterm birth, 5-min Apgar <7, admission to neonatal ICU/special care nursery and large for gestational age infant but was not associated with gestational diabetes, Cesarean section, perinatal death or SGA.

Trauman et al. [52] prospectively studied 74 pregnant women (24% with OSA) and full-term infants for general movements and neurodevelopment at 48 h, 8–11 weeks, 14–16 weeks and at 12 months. Infant developmental inventory and infant brief questioner were administered. At 12 months, 64% of infants born to SDB mothers showed low social developmental score as compared to 25% of infants born to controls (P = 0.36, odds ratio 16.7). In neonatal and infant neuromotor development, there was no difference between infants born to SBD mother or controls. Another study that failed to show negative outcome was by Bassan et al. [53]. The group studied 44 women (25% had SDB) with full-term infants showed that there was no difference in birthweight, gestational age, 5-min APGAR score and neurological exam score between infants born to SDB and non-SDB mothers.

Ravishankar et al. [54] studied the effect of SDB on histopathology and immune-histochemical markers of placental perfusion and hypoxia. The placentas of women with OSA (n = 23), habitual snoring (n = 78) and non-snorers (n = 47) were accessed. Fetal normoblastemia was prevalent in OSA as compared to snorers and controls (56.5%, 34.6%, and 6.4% respectively). Increased tissue hypoxia marker, carbonic anhydrase IX immunoreactivity, was demonstrated in OSA pregnant women as compare to non-snorers and controls (81.5%, 91.3% and 57.5%, respectively). Uteroplacental and reperfusion score were similar in all groups.

Further studies in the future are warranted to assess the effect of SDB on perinatal and neonatal outcome.

#### **8. Update on oral appliance therapy**

In 2015, the AASM published an update of clinical practice guidelines of treatment for OSA and snoring with oral appliance therapy [55]. The new guidelines continued to recommend oral appliance therapy and gave increased focus on patient preference. An oral appliance can now be considered for all levels of OSA severity (mild, moderate and severe), if the patient fails or refuses CPAP, or even if they simply prefer an oral appliance to CPAP.

Subjective adherence with oral appliance therapy is better overall than objective adherence with CPAP in adult patients with OSA. CPAP is superior to oral appliance therapy in improving the AHI and lowering the arousal index and the ODI, but the new guidelines suggest that the overall therapeutic effectiveness of oral appliances may be comparable with CPAP because of the significant difference in adherence rates.

These new guidelines recommend that sleep physicians prescribe oral appliances for patients who request treatment of primary snoring. When prescribed for OSA patient, it suggests that a qualified dentist use a custom, titratable appliance. It also recommends that sleep physicians consider prescription of oral appliances for adult patients with OSA who are intolerant of CPAP therapy or prefer alternate therapy. Qualified dentists should provide oversight of oral appliance therapy in OSA patients, and sleep physicians should conduct follow-up sleep testing to improve or confirm treatment efficacy.

Studies have demonstrated efficacy of oral appliance therapy comparable to CPAP in selected patients [56, 57]. While oral appliances help to decrease AHI/RDI/REI across all severity levels, there are few reported factors that consistently predict improvement in OSA using oral appliances. A number of possible predictors have been examined. Among these are changes in pharyngeal geometry under drug-induced sleep endoscopy (DISE) [58] and nasoendoscopy to assess velopharynx/oro/hypopharyngeal geometry [59]. In the study of Gjerde [57], low oxygen levels carried a high predictive value for failure with oral appliance therapy.

### **9. Update on PAP devices**

neuromotor development, there was no difference between infants born to SBD mother or controls. Another study that failed to show negative outcome was by Bassan et al. [53]. The group studied 44 women (25% had SDB) with full-term infants showed that there was no difference in birthweight, gestational age, 5-min APGAR score and neurological exam score

Ravishankar et al. [54] studied the effect of SDB on histopathology and immune-histochemical markers of placental perfusion and hypoxia. The placentas of women with OSA (n = 23), habitual snoring (n = 78) and non-snorers (n = 47) were accessed. Fetal normoblastemia was prevalent in OSA as compared to snorers and controls (56.5%, 34.6%, and 6.4% respectively). Increased tissue hypoxia marker, carbonic anhydrase IX immunoreactivity, was demonstrated in OSA pregnant women as compare to non-snorers and controls (81.5%, 91.3% and

Further studies in the future are warranted to assess the effect of SDB on perinatal and neo-

In 2015, the AASM published an update of clinical practice guidelines of treatment for OSA and snoring with oral appliance therapy [55]. The new guidelines continued to recommend oral appliance therapy and gave increased focus on patient preference. An oral appliance can now be considered for all levels of OSA severity (mild, moderate and severe), if the patient

Subjective adherence with oral appliance therapy is better overall than objective adherence with CPAP in adult patients with OSA. CPAP is superior to oral appliance therapy in improving the AHI and lowering the arousal index and the ODI, but the new guidelines suggest that the overall therapeutic effectiveness of oral appliances may be comparable with CPAP

These new guidelines recommend that sleep physicians prescribe oral appliances for patients who request treatment of primary snoring. When prescribed for OSA patient, it suggests that a qualified dentist use a custom, titratable appliance. It also recommends that sleep physicians consider prescription of oral appliances for adult patients with OSA who are intolerant of CPAP therapy or prefer alternate therapy. Qualified dentists should provide oversight of oral appliance therapy in OSA patients, and sleep physicians should conduct follow-up sleep

Studies have demonstrated efficacy of oral appliance therapy comparable to CPAP in selected patients [56, 57]. While oral appliances help to decrease AHI/RDI/REI across all severity levels, there are few reported factors that consistently predict improvement in OSA using oral appliances. A number of possible predictors have been examined. Among these are changes in pharyngeal geometry under drug-induced sleep endoscopy (DISE) [58] and nasoendoscopy to assess velopharynx/oro/hypopharyngeal geometry [59]. In the study of Gjerde [57], low oxygen levels carried a high predictive value for failure with oral appliance therapy.

fails or refuses CPAP, or even if they simply prefer an oral appliance to CPAP.

57.5%, respectively). Uteroplacental and reperfusion score were similar in all groups.

between infants born to SDB and non-SDB mothers.

**8. Update on oral appliance therapy**

because of the significant difference in adherence rates.

testing to improve or confirm treatment efficacy.

natal outcome.

10 Sleep Apnea - Recent Updates

Many types of PAP devices are used to treat the whole spectrum of SDB including CPAP, autotitrating CPAP (APAP), bilevel PAP, autotitrating bilevel PAP, volume-assured pressure support and adaptive servoventilation. CPAP and APAP are most commonly used, whereas the other modes are reserved for patients needing respiratory assist. For CPAP and APAP, data collection systems can track compliance, pressure, leak and efficacy. Refer to Johnson et al. [60] for a comprehensive review of the technological aspects of PAP devices in general with its algorithms, including event detection, sampling rates, cycling, targets, rate and pressure adjustments as well as suggested settings.

APAP has been shown to be an effective means to determine therapeutic CPAP levels. The question remains as to whether APAP is suitable for long-term treatment of patients with OSA. In the past 5 years, at least three different meta-analyses [61–63] have been performed comparing the efficacy of CPAP to APAP and demonstrating similar effectiveness. These three studies have found that APAP and CPAP produce comparable reductions in AHI, decreased sleepiness, comparable long-term compliance and improvements in sleep architecture. Because the treatment effects are similar between APAP and CPAP, the therapy of choice may depend on other factors such as patient preference, specific reasons for non-compliance and cost [62].

Although CPAP and APAP appear comparable, other investigators have looked at the use of alternative PAP modalities presumed to be more comfortable for therapy in OSA patients with the hopes of leading to improved compliance. These have included auto-bi-level pressure relief-positive airway pressure (ABPR-PAP). Four studies [64–67] showed similar improvements in symptoms using an auto-bi-level mode and CPAP. Compliance was generally better with the auto-bi-level modes than CPAP, even in CPAP patients selected for poor compliance [65, 67].

#### **10. Emerging concepts**

Over the past 5 years, there continues to be advancement in understanding of all aspects of OSA. Major categories have been covered in other areas of this review. Below is a selected group of topics with new emerging points of view that deserve increased focus in the future.

#### **10.1. Interventions to improve CPAP compliance**

CPAP is not accepted by many users. Educational, supportive and behavioral interventions may help people with OSA recognize the need for regular and continued use of CPAP. An updated review on the effect of these intervention modalities was performed in 2014. Thirty randomized controlled studies (2047 participants) were included [68]. Low-to-moderate quality evidence showed that all three types of interventions led to increased machine usage in CPAP-naive patients with moderate-to-severe OSA. Compared with usual care, supportive ongoing interventions increased CPAP use by 50 min per night and increased the number of patients who used CPAP for longer than 4 hours per night from 59% to 75%. Educational interventions increased CPAP use by 35 min per night and increased the number of patients who used CPAP for longer than 4 h per night from 57% to 70%. Behavioral therapy led to an improvement in CPAP use of 1.44 h per night and increased the number of patients who used CPAP for longer than four hours per night from 28% to 47%.

#### **10.2. More focus on the relationship between smoking and OSA**

It has been suspected for some time that smoking and OSA adversely affect each other, leading to increased comorbidity; however, this is still a matter of debate. There seems to be a synergistic effect between smoking and OSA, which may lead to increase in cardiovascular morbidity [69]. However, the evidence is less than conclusive. Cigarette smoking may increase the severity of OSA through alterations in sleep architecture, upper airway neuromuscular function, arousal mechanisms and upper airway inflammation. And untreated OSA may be associated with smoking addiction. The effect of smoking cessation on OSA remains to be determined. Future studies are needed in order to establish the strength of the association of both conditions [70].

#### **11. Patient-specific therapy and customization of therapy**

#### **11.1. Focus on pathophysiology**

There has been an increased focus on the importance of pathophysiological factor identification for customized therapy in OSA patients and more investigation of different group phenotypes or individual characteristics to personalize OSA therapy. Differentiated OSA phenotypes have been proposed: a small pharyngeal airway with a low resistance to collapse (increased critical closing pressure), an inadequate response of pharyngeal dilator muscles (wakefulness drive to breathe), an unstable ventilator responsiveness to hypercapnia (high loop gain) and an increased propensity to wake related to upper airway obstruction (low arousal threshold) [71]. If an accurate pathophysiological pattern for each OSA patient can be identified, customized—and presumably more effective—therapy would potentially be feasible [71].

A large cohort of 1249 patients (age 47 years; AHI 18.9/h; BMI 27.2 ± 3.7 kg/m2 ) underwent PSG and DISE to determine upper airway (UA) collapse patterns [72]. Palatal collapse was the most frequent (81%). Multilevel collapse was noted in 68.2% of patients; the most frequent multilevel pattern was a combination of palatal and tongue base collapse (25.5%). The prevalence of complete collapse, multilevel collapse and hypopharyngeal collapse increased with increasing severity of obstructive sleep apnea (OSA). Multilevel and complete collapses were more prevalent in obese patients and in those with more severe OSA. Both higher BMI and AHI values were associated with a higher probability of complete concentric palatal collapse. However, UA collapse patterns during DISE cannot be fully explained by selected baseline polysomnographic and anthropometric characteristics.

Age may play a significant role. A study in which 10 young (20–40 year) and old (60 year and older) patients with OSA matched by BMI and sex suggested that airway anatomy/collapsibility plays a relatively greater pathogenic role in older adults whereas sensitive ventilatory control system is more prominent trait in younger adults [73].

#### **11.2. Focus on mild OSA**

who used CPAP for longer than 4 h per night from 57% to 70%. Behavioral therapy led to an improvement in CPAP use of 1.44 h per night and increased the number of patients who used

It has been suspected for some time that smoking and OSA adversely affect each other, leading to increased comorbidity; however, this is still a matter of debate. There seems to be a synergistic effect between smoking and OSA, which may lead to increase in cardiovascular morbidity [69]. However, the evidence is less than conclusive. Cigarette smoking may increase the severity of OSA through alterations in sleep architecture, upper airway neuromuscular function, arousal mechanisms and upper airway inflammation. And untreated OSA may be associated with smoking addiction. The effect of smoking cessation on OSA remains to be determined. Future studies are needed in order to establish the strength of the associa-

There has been an increased focus on the importance of pathophysiological factor identification for customized therapy in OSA patients and more investigation of different group phenotypes or individual characteristics to personalize OSA therapy. Differentiated OSA phenotypes have been proposed: a small pharyngeal airway with a low resistance to collapse (increased critical closing pressure), an inadequate response of pharyngeal dilator muscles (wakefulness drive to breathe), an unstable ventilator responsiveness to hypercapnia (high loop gain) and an increased propensity to wake related to upper airway obstruction (low arousal threshold) [71]. If an accurate pathophysiological pattern for each OSA patient can be identified, custom-

PSG and DISE to determine upper airway (UA) collapse patterns [72]. Palatal collapse was the most frequent (81%). Multilevel collapse was noted in 68.2% of patients; the most frequent multilevel pattern was a combination of palatal and tongue base collapse (25.5%). The prevalence of complete collapse, multilevel collapse and hypopharyngeal collapse increased with increasing severity of obstructive sleep apnea (OSA). Multilevel and complete collapses were more prevalent in obese patients and in those with more severe OSA. Both higher BMI and AHI values were associated with a higher probability of complete concentric palatal collapse. However, UA collapse patterns during DISE cannot be fully explained by selected baseline

Age may play a significant role. A study in which 10 young (20–40 year) and old (60 year and older) patients with OSA matched by BMI and sex suggested that airway anatomy/collapsibility plays a relatively greater pathogenic role in older adults whereas sensitive ventilatory

) underwent

ized—and presumably more effective—therapy would potentially be feasible [71]. A large cohort of 1249 patients (age 47 years; AHI 18.9/h; BMI 27.2 ± 3.7 kg/m2

polysomnographic and anthropometric characteristics.

control system is more prominent trait in younger adults [73].

CPAP for longer than four hours per night from 28% to 47%.

tion of both conditions [70].

12 Sleep Apnea - Recent Updates

**11.1. Focus on pathophysiology**

**10.2. More focus on the relationship between smoking and OSA**

**11. Patient-specific therapy and customization of therapy**

There remains to be a debate about how significant is the effect of mild OSA on adverse health outcomes, to the point that unless not accompanied by specific medical conditions or symptoms, insurers will not cover therapy. The American Thoracic Society in 2016 published a research statement hoping to find answers to this lingering question [74]. The specific goals of this statement were to appraise the evidence regarding whether long-term adverse neurocognitive and cardiovascular outcomes are attributable to mild OSA and evaluate whether or not treatment of mild OSA is effective at preventing or reducing these adverse outcomes.

Unfortunately, studies were incongruent in their definitions of mild OSA, and data were inconsistent regarding the relationship between mild OSA and daytime sleepiness. It was concluded that treatment of mild OSA may improve sleepiness in patients who are sleepy at baseline and improve quality of life. There was limited or inconsistent evidence pertaining to the impact of therapy of mild OSA on other adverse outcomes.

#### **11.3. More focus on perioperative care of OSA patients**

The Society of Anesthesia and Sleep Medicine published in 2016 guidelines on preoperative screening and assessment of OSA patients [75]. This guideline emphasizes again the increased risks of perioperative complications in patients with OSA and recommended that practice groups consider making OSA screening a standard part pre-anesthetic evaluation. It did not go as far as recommending cancelling or delaying surgery to diagnose OSA unless there is evidence of an associated significant or uncontrolled systemic disease or additional problems with ventilation or gas exchange. The use of PAP therapy in previously undiagnosed, but suspected OSA patients should be considered case by case. Continued use of PAP therapy at previously prescribed settings in OSA is recommended during periods of sleep while hospitalized, both preoperatively and postoperatively. These guidelines strongly recommended for protocols for known or suspected OSA to be developed by individual institutions taking into account the patients' conditions, extent of interventions and available resources.

#### **11.4. More focus on commercial motor vehicle OSA screening and treatment**

A recommendation overview of commercial motor vehicle OSA screening and treatment was published in 2016. This document goes over prior recommendations and details the small differences present in other statements regarding this topic. There is a need for federal regulations to clarify the issue. Among the recommendations by the authors are the following [76]:

Out of service evaluation is recommended when admitted sleepiness while driving, motor vehicle collision attributable to falling asleep, ESS score >10, and OSA without objective documentation of sufficient therapy efficacy and/or adherence for OSA testing. PSG is preferred diagnostic test; however, HST may be a reasonable alternative in selected patients based on the sleep specialist assessment.

AHI, RDI or REI >20/h are recommended to have treatment. PAP therapy is generally the most expeditious treatment available. Surgical evaluation may be considered based on comprehensive assessment findings. Weight loss is recommended as adjunct. AHI, RDI or REI ≥5/h with sleepiness or sleepiness-related accident should be counseled to initiate treatment for OSA.

Documentation of efficacy of therapy is recommended. PAP therapy usage below published minimum recommendations (≥4 h for ≥70% of nights) could result in removal from service by the certified medical examiner. PAP therapy adherence should be objectively monitored by a sleep specialist assessing therapy adherence and efficacy. Printed reports of therapy adherence data should be made available to the certified medical examiner.

#### **Author details**

Samson Z. Assefa\*, Monteserrat Diaz-Abad and Steven M. Scharf

\*Address all correspondence to<EMAIL_ADDRESS>
University of Maryland, Baltimore, USA

#### **References**

[8] Berry RB, Brooks R, Gamaldo CE, et al. for the American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.2, www.aasmnet.org, American Academy of Sleep Medicine, Darien, IL 2015. http://www.aasmnet.org/scoringmanual/v2.2.0/html/ index.html

prehensive assessment findings. Weight loss is recommended as adjunct. AHI, RDI or REI ≥5/h with sleepiness or sleepiness-related accident should be counseled to initiate treatment

Documentation of efficacy of therapy is recommended. PAP therapy usage below published minimum recommendations (≥4 h for ≥70% of nights) could result in removal from service by the certified medical examiner. PAP therapy adherence should be objectively monitored by a sleep specialist assessing therapy adherence and efficacy. Printed reports of therapy adher-

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**Chapter 2 Provisional chapter**

**Nerve and Muscle Changes in the Upper Airways of Subjects with Obstructive Sleep Apnea: Structural Basis for the Neurogenic Theory Nerve and Muscle Changes in the Upper Airways of Subjects with Obstructive Sleep Apnea: Structural Basis for the Neurogenic Theory**

Teresa Cobo, Emilio Macías, Covandonda Álvarez, Alberto Álvarez-Suárez, Felix de Carlos, Jose A. Vega and Juan Cobo Covandonda Álvarez, Alberto Álvarez-Suárez, Felix de Carlos, Jose A. Vega and Juan Cobo

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/65438

Teresa Cobo, Emilio Macías,

#### **Abstract**

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20 Sleep Apnea - Recent Updates

Sleep Med 2016 Jan;12(1):113–125.

Obstructive sleep apnea syndrome (OSAS) is a widely diffused disease associated with specific genetics, age, gender, craniofacial and upper airways anatomy, obesity, and endocrine conditions, but not with ethnicity profiles. The so‐called neurogenic neuro‐ genic theory of OSAS postulates that the collapse of the upper airways that characterize this disease is due to peripheral nerve degeneration that leads to muscle atrophy and collapse. This review attempts to summarize the structural and functional changes in both the sensory and motor innervation of the walls of the upper air ways in patients suffering from OSAS.

**Keywords:** peripheral neuropathy, nerve fibers, mechanoreceptors, skeletal muscles, obstructive sleep apnea syndrome

#### **1. Introduction**

Obstructive sleep apnea syndrome (OSAS) is a common chronic disease characterized by sleep fragmentation due to apnea‐hypoapnea and repeated arousal [1]. OSAS afflicts 2–4% of the population and has a strong genetic component [2]. Moreover, age, gender, craniofacial structure and the anatomy of the upper airways (UA), endocrine conditions, and obesity, but not ethnicity, are associated with OSAS [3].

and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The collapse of UA during sleep is the major characteristic of OSAS [4]. Two primary theories have been proposed to explain the pathophysiology of OSAS: the obstructive theory, in which muscle hypertrophy leads to airway narrowing, and the neurogenic theory, which postulates that peripheral nerve degeneration due to vibratory stretch trauma, or systemic diseases, lead to muscle atrophy and collapse [5–7]. A progressive local neurogenic lesion caused by repeated microtrauma of snoring might be a potential contribution factor for UA collapsibility [8].

The UA size and resistance are tightly regulated by neural mechanisms that control muscles and reflexes. The sensory nerve endings in the mucosa and mechanoreceptors of UA walls respond to changes in different sensory modalities (light touch, temperature, pressure, pain, muscle stretch and proprioception, water and chemical stimuli). Sensory inputs from these structures continually streams toward the central nervous system, including respiratory centers, which control UA muscles via efferent motor neural outputs [9], thus adjusting the contraction of the UA muscles during sucking, swallowing, respiration, speech, and mastica‐ tion, as well as gagging, vomiting, coughing, and snoring reflexes (**Figure 1**). The structural

**Figure 1.** Schematic representation of the afferent and efferent innervation of the upper air ways. The mucosa of the nasopharynx, oropharynx, and laryngopharynx are primarily supplied by the trigeminus (maxillary division, V2), glossopharyngeal (IX), and vagus (X) cranial nerves, respectively, with a minimal contribution of the facial nerve (VII). The sensory neurons of these nerves are placed in the parent sensory ganglia and their central processes synapse within the trigeminal and tractus solitarius nuclei of the brain stem. These nuclei send the inputs to nuclei whose motoneurons are located in the pons trigeminal, facial, ambiguous, and hypoglossal nuclei, and in the anterior horn of the cervical spinal cord (C1‐2). Axons from these motor neurons travel through cranial nerves V, VII, IX to XII, and the ansa cervicalis and form motor endplates that use acetylcholine for neurotransmission via nicotinic receptors to innervate innervating UA muscles (for a more detailed description of these muscles and nerves, see Massey [68]).

support of these reflexes consists of sensory receptors connected with sensory nerve fibers, the central synaptic connections that almost always use interneurons, and the efferent path‐ way composed of the motoneurons, innervating the effector organ. The effector organ in a somatic reflex is the striated muscle innervated by the α‐motoneurons [10].

#### **2. The neurological theory of OSAS and the upper airways remodeling**

In the last decade of twentieth century, Woodson et al. [11] hypothesized that the pathophysi‐ ologic events that lead to the development of airway instability may be secondary to modifica‐ tions in neurologic control, airway morphology, or both. Changes in sensation, muscle structure, and physiological properties of UA have been reported in patients with OSAS; these changes are referred to as airway remodeling. But whereas the structural and functional properties of muscles of OSAS patients have been extensively analyzed [5, 12–14], the motor nerve fibers and motor endplates as well as the potential role of sensory nerve impairment in OSAS have not been sufficiently investigated [15, 16]. Furthermore, the available data are heterogeneous and sometimes contradictory, because of the heterogeneity of the UA muscles, the different nerves innervating these muscles and the UA mucosae, and the differences in the methods used.

The nerve and muscle characteristics of OSAS patients may result from complex interactions of vibratory stretch trauma, inflammation, and hypoxia [8, 15–20]. It has been proposed that the repeated mechanical trauma and/or hypoxemia associated with OSA may lead to sensory and motor impairment of upper airway structures [8, 21], or that local nerve lesions due to long‐stand‐ ing snoring vibrations could be the basis of OSAS or its progression [17, 22, 23]. But is the neu‐ ropathy of OSAS, the cause or a consequence of the disease? It is unknown to what extent chronic intermittent hypoxemia in OSAS causes damage to the motor and sensory peripheral nerve, but muscle action potential and sensory nerve action potential amplitudes are significantly reduced in the nerves outside UA in patients with OSAS suggesting that axonal damage exists in patients with OSAS to a greater extent than previously thought [24]. On the other hand, association between OSAS and sensory neuropathy, and nerve damage outside the UA [18, 25–27], type 2 or type 1A diabetic neuropathy, and axonal subtypes of Charcot‐Marie‐Tooth disease [28–31] has been also demonstrated. Of particular interest is the epidemiological association between OSAS and ante‐ rior ischemic optic neuropathy [32] although a concluding rapport cannot be established [33].

Thus, there is a large body of evidence that UA neuromuscular abnormalities are frequent in OSAS patients, and these altogether support the neurogenic theory of OSAS [5–7, 34]. In recent years, multiple studies have demonstrated altered UA sensory input and abnormal UA motor function in patients with OSAS using a variety of neurophysiological and histo‐ logical approaches [5, 7, 35–37], and impaired neural function is at least partly reversible with treatment for sleep apnea [27].

#### **3. Nerve changes in OSAS**

**Figure 1.** Schematic representation of the afferent and efferent innervation of the upper air ways. The mucosa of the nasopharynx, oropharynx, and laryngopharynx are primarily supplied by the trigeminus (maxillary division, V2), glossopharyngeal (IX), and vagus (X) cranial nerves, respectively, with a minimal contribution of the facial nerve (VII). The sensory neurons of these nerves are placed in the parent sensory ganglia and their central processes synapse within the trigeminal and tractus solitarius nuclei of the brain stem. These nuclei send the inputs to nuclei whose motoneurons are located in the pons trigeminal, facial, ambiguous, and hypoglossal nuclei, and in the anterior horn of the cervical spinal cord (C1‐2). Axons from these motor neurons travel through cranial nerves V, VII, IX to XII, and the ansa cervicalis and form motor endplates that use acetylcholine for neurotransmission via nicotinic receptors to innervate innervating

The collapse of UA during sleep is the major characteristic of OSAS [4]. Two primary theories have been proposed to explain the pathophysiology of OSAS: the obstructive theory, in which muscle hypertrophy leads to airway narrowing, and the neurogenic theory, which postulates that peripheral nerve degeneration due to vibratory stretch trauma, or systemic diseases, lead to muscle atrophy and collapse [5–7]. A progressive local neurogenic lesion caused by repeated microtrauma of snoring might be a potential contribution factor for UA collapsibility [8].

22 Sleep Apnea - Recent Updates

The UA size and resistance are tightly regulated by neural mechanisms that control muscles and reflexes. The sensory nerve endings in the mucosa and mechanoreceptors of UA walls respond to changes in different sensory modalities (light touch, temperature, pressure, pain, muscle stretch and proprioception, water and chemical stimuli). Sensory inputs from these structures continually streams toward the central nervous system, including respiratory centers, which control UA muscles via efferent motor neural outputs [9], thus adjusting the contraction of the UA muscles during sucking, swallowing, respiration, speech, and mastica‐ tion, as well as gagging, vomiting, coughing, and snoring reflexes (**Figure 1**). The structural

UA muscles (for a more detailed description of these muscles and nerves, see Massey [68]).

Consistent with the above data, studies on the innervation of the palate‐pharyngeal region in OSAS patients have revealed both increased and decreased number of nerves in the mucosa

**Figure 2.** Main changes in nerves and muscles in OSAS patients in comparison with non‐OSAS subjects. Data are based on the text and the figures are a courtesy of J.A. Vega.

and muscles [8, 13, 38, 39], as well as degenerative changes in myelinated and unmyelinated nerve fibers [40], and the degree of sensory neuropathy in UA correlates with the degree of OSAS (**Figure 2**) [41].

#### **3.1. The afferent system: functional and structural data**

If the anatomically deeper motor axons are affected by UA vibration, sensory afferents closer to the airway surface should also be impaired thus impairing normal inputs for reflex mecha‐ nisms which contribute to the upper airway function. Nevertheless, the evidence supporting sensory nerve impairment in OSA is less convincing than that for motor nerves.

The mucosal sensory function is impaired at multiple UA sites in OSAS [16]. Focal degenera‐ tion of myelinated and nonmyelinated nerve fibers, affecting Schwann cells and axons in the soft palate and uvula have been demonstrated in OSAS patients [11, 40]. In these UA zones, increase in the density of epithelial afferent nerve endings (based on the expression of sub‐ stance P and calcitonin gene‐related peptide) was also observed which is indicative of nerve lesion [38]. On the other hand, the afferent information from UA muscles is important in regu‐ lating the masticatory force and oromotor behaviors, but also in the response of important reflexes related to speech, swallowing, cough, vomit, or normal breathing [10, 42]. Patients with OSAS show a significant reduction in the density of nerve fibers in the submucosa as well as morphological abnormalities in mechanosensory corpuscles. Importantly, the muscle innervation of nerve fibers expressing ASIC2 and TRPV4 (regarded as two putative mechano‐ proteins) is also reduced in these subjects [43].

In addition, and consistently with the above‐mentioned pathological findings, UA sensory function has been shown to be impaired in OSAS during wakefulness [13, 15, 16, 44], specially patients with OSAS have altered vibration and cold detection thresholds [45, 46]. The respira‐ tory‐related evoked potentials (RREPs) during wakefulness in OSA revealed a reduction in the amplitude but not the latency of the early RREP components [44, 47] reflecting sensory processing is reduced in the OSA patients [48]. Other studies revealed no changes [49–51].

#### **3.2. The efferent system: functional and structural data**

Data regarding the changes in motor nerves during OSAS are scarce. Motor neuron lesions and/or direct damage in the muscles [17, 41] as well a decrease [39, 43] or increase [13] in the number of nerve fibers have been reported. But most studies have focused directly on muscles.

#### **4. Muscle changes in OSAS**

and muscles [8, 13, 38, 39], as well as degenerative changes in myelinated and unmyelinated nerve fibers [40], and the degree of sensory neuropathy in UA correlates with the degree of

**Figure 2.** Main changes in nerves and muscles in OSAS patients in comparison with non‐OSAS subjects. Data are based

If the anatomically deeper motor axons are affected by UA vibration, sensory afferents closer to the airway surface should also be impaired thus impairing normal inputs for reflex mecha‐ nisms which contribute to the upper airway function. Nevertheless, the evidence supporting

The mucosal sensory function is impaired at multiple UA sites in OSAS [16]. Focal degenera‐ tion of myelinated and nonmyelinated nerve fibers, affecting Schwann cells and axons in the soft palate and uvula have been demonstrated in OSAS patients [11, 40]. In these UA zones, increase in the density of epithelial afferent nerve endings (based on the expression of sub‐ stance P and calcitonin gene‐related peptide) was also observed which is indicative of nerve lesion [38]. On the other hand, the afferent information from UA muscles is important in regu‐ lating the masticatory force and oromotor behaviors, but also in the response of important reflexes related to speech, swallowing, cough, vomit, or normal breathing [10, 42]. Patients with OSAS show a significant reduction in the density of nerve fibers in the submucosa as well as morphological abnormalities in mechanosensory corpuscles. Importantly, the muscle innervation of nerve fibers expressing ASIC2 and TRPV4 (regarded as two putative mechano‐

sensory nerve impairment in OSA is less convincing than that for motor nerves.

OSAS (**Figure 2**) [41].

24 Sleep Apnea - Recent Updates

**3.1. The afferent system: functional and structural data**

on the text and the figures are a courtesy of J.A. Vega.

proteins) is also reduced in these subjects [43].

Structural changes in skeletal muscles have been studied primarily in the uvula muscles and the palatopharyngeal muscle, and the reported changes are very heterogeneous. They include focal muscle atrophy and muscle bundle disruption [11, 52], prevalence of angulated mus‐ cle fibers, increased and/or reduction of muscle fibers diameter and variation in fiber type grouping [14, 23, 53–57], atrophic and hypertrophic muscle fibers [8, 52, 58], changes in mito‐ chondria content [14], enzymatic changes [56], and increased neural cell adhesion molecule expression by muscle cells [13].

Another characteristic of the UA muscles is the high percentage disproportion of glycolytic fast twitch of type II muscle fibers compared with non‐OSA control subjects [12, 14, 55, 59–62], a difference that may represent an adaptive response to mechanical strain and/or neuronal activity. In this way, the over expression of N‐CAM is suggestive of collateral nerve sprout‐ ing, reflected in the hyperinnervation that present these muscles [13]. Vascular enlargement, fibrosis, edema, inflammatory cells, and infiltration have also been reported. There is also increased fat in and around the muscles of the UA in patients with OSA [63].

However, all these changes in muscle fibers are not a major contributing factor to OSAS pathogenesis in most patients [20].

In addition to the structural changes, the UA muscles also show electrophysiological changes in OSAS. Patients with OSA have higher levels of multiunit electromyographic activity (EMG) recorded in the UA muscles compared to healthy control subjects presumably secondary to neurogenic remodeling. This is characterized by chronic partial denervation of muscle fibers, with reinnervation of the orphaned muscle fibers by collateral sprouting of surviving motor axons [13, 60, 64–66]. The apparent increase in drive was ascribed to a neural compensation for a narrow UA.

Recent investigations using single motor unit techniques have shown that the motor unit potentials of upper airway muscles in OSA patients are larger in area, longer in duration, and more complex [7, 23, 37]. These changes could contribute to the increased multiunit EMG in OSAS. However, the presence of denervation and subsequent axonal sprouting may lead to changes in fine motor control such as speech [67].

#### **5. Concluding remarks and future perspectives**

The involvement of the peripheral nervous system and muscles in the pathogenesis of OSAS is now accepted. Nevertheless, large have been reported presumably due to the method‐ ological differences used to evaluate both the pathological and functional changes in UA of patients suffering from OSAS. So, whereas some researchers found decrease in the density of nerve fibers [39, 40, 43] some others have found increased numbers of nerve fibers in the mucosa and muscles [13, 38]. These discrepancies can be related to the zones of the UA sampled or the muscles analyzed. And more importantly, no specific markers for sensory or motor nerve fibers were used in these studies. Another important aspect is the studies about the state of motor end‐plates in OSAS. Thus, further studies are required to elucidate the role of upper airway sensory and motor impairment in modulating disease progression or severity.

#### **Author details**

Teresa Cobo1,2, Emilio Macías1,2, Covandonda Álvarez1,2, Alberto Álvarez‐Suárez3 , Felix de Carlos2 , Jose A. Vega4 \* and Juan Cobo1,2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Orthodontics Area ‐ Instituto Asturiano de Odontología, Oviedo, Spain

2 Department of Surgery and Medical‐Surgical Specialties (Section of Odontology), University of Oviedo, Spain

3 Mechanical Engineering Area – Instituto Asturiano de Odontología, Oviedo, Spain

4 Department of Morphology and Cell Biology, SINPOS Group, University of Oviedo, Spain

#### **References**

[4] Eckert DJ, Malhotra A. Pathophysiology of adult obstructive sleep apnea. Proc Am Thorac Soc. 2008; 5: 144–153. DOI: 10.1513/pats.200707‐114MG.

more complex [7, 23, 37]. These changes could contribute to the increased multiunit EMG in OSAS. However, the presence of denervation and subsequent axonal sprouting may lead to

The involvement of the peripheral nervous system and muscles in the pathogenesis of OSAS is now accepted. Nevertheless, large have been reported presumably due to the method‐ ological differences used to evaluate both the pathological and functional changes in UA of patients suffering from OSAS. So, whereas some researchers found decrease in the density of nerve fibers [39, 40, 43] some others have found increased numbers of nerve fibers in the mucosa and muscles [13, 38]. These discrepancies can be related to the zones of the UA sampled or the muscles analyzed. And more importantly, no specific markers for sensory or motor nerve fibers were used in these studies. Another important aspect is the studies about the state of motor end‐plates in OSAS. Thus, further studies are required to elucidate the role of upper airway sensory and motor impairment in modulating disease progression

Teresa Cobo1,2, Emilio Macías1,2, Covandonda Álvarez1,2, Alberto Álvarez‐Suárez3

3 Mechanical Engineering Area – Instituto Asturiano de Odontología, Oviedo, Spain

2 Department of Surgery and Medical‐Surgical Specialties (Section of Odontology), University

4 Department of Morphology and Cell Biology, SINPOS Group, University of Oviedo, Spain

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#### **Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome**

Daniel Álvarez, Ana Cerezo-Hernández, Graciela López-Muñiz, Tania Álvaro-De Castro, Tomás Ruiz-Albi, Roberto Hornero and Félix del Campo Daniel Álvarez, Ana Cerezo-Hernández, Graciela López-Muñiz, Tania Álvaro-De Castro, Tomás Ruiz-Albi, Roberto Hornero and Félix del Campo

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66570

#### **Abstract**

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients' needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed.

**Keywords:** sleep apnea-hypopnea syndrome, pattern recognition, automated biomedical signal processing, artificial neural networks, multilayer perceptron, feedforward back-propagation, Bayes theory

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#### **1. Introduction**

In their daily practice, physicians must frequently decide a definitive diagnosis or the most suitable treatment using several variables from multiple clinical data sources, which is a highly complex task. A huge amount of valuable healthcare-related information is currently available, from symptoms reported by the patient and details stored in their clinical history to biochemical data and outcomes from biomedical recordings or medical images. In this context, machine learning methods are essential to maximize the usefulness of medical data in order to expedite decisions and avoid misdiagnosis. In the last decades, the increasing development of computers and artificial intelligence has led to the use of decision support expert systems in the common clinical practice of several fields of medicine [1, 2]. The huge number of studies published in the context of biomedical engineering during the last years clearly shows this trend.

Bayesian theory was one of the first mathematical frameworks used to implement decision support systems. Regarding the classification of an item, according to the Bayes' decision rule, the predicted class must be the one that maximizes *a posteriori* probability in order to minimize the classification error. A major goal is to model the statistical characteristics of the problem under study, leading to expert systems able to assist physicians in decision-making processes. Among pattern recognition algorithms, conventional statistical classifiers, such as discriminant analysis [3] or logistic regression (LR) [4], and more recently artificial neural networks (ANNs) [5], have been widely applied. The widely known statistical classifiers assume that the class density function of input data is known *a priori*. Assumptions such as normal distribution, homoscedasticity, linearity, independency, or stationarity decrease the complexity of the classifier, minimize the classification error, and improve the performance. Nevertheless, these assumptions are not always consistent in real-world pattern classification problems, especially when working with limited datasets. Conversely, when using ANNs, no assumptions are made about the probability density functions of input features and the training data is used directly to optimize the decision rule [6]. Nevertheless, ANNs are characterized by a complex design stage. Both statistical and ANNs approaches have its advantages and limitations. However, the ability to model complex nonlinear problems, which are very common in biological systems, have made ANNs widely used in medical applications.

The first attempt to model information processing in biological systems by means of ANNs was carried out by McCulloch and Pitts in 1943 [7]. Since then, ANN-based algorithms have significantly evolved and their use in the field of medicine has increased considerably, particularly since the late 1990s. Some computer programs in the context of statistical medicine already include ANNs among their functionalities, which has contributed to increase their use in medical research. Nevertheless, "neural network" remains frequently a confusing term for many healthcare-related researchers. The implementation of an ANN has to be carried out by means of advanced software and some expertise is required to set up properly the userdependent input parameters. However, once designed, they are reliable and easy to use tools, even by nontrained personnel. In addition, once optimized, the computational time is small, which is a major feature in order to speed up decision making.

Sleep research and particularly sleep-related breathing disorders (SBD) is a field in which the application of automated pattern recognition algorithms has increased exponentially during the last years due to the need for automating their complex diagnostic processes. Particularly challenging is the management of sleep apnea-hypopnea syndrome (SAHS). The gold standard technique for SAHS diagnosis is in-lab nocturnal polysomnography (PSG). During PSG, several neuromuscular and cardiorespiratory signals (up to 32 biomedical recordings) are monitored and stored for subsequent interpretation by trained personnel, which is a highly complex and time-consuming task [8]. In addition, accessibility to diagnosis and treatment is limited due to insufficient resources, both human (trained specialists) and technical (specialized sleep units), which have led to large waiting lists [9]. In this context, automated computer-aided diagnosis systems have emerged as very useful tools to deal with complex rules involving several biomedical recordings simultaneously, in order to expedite diagnosis and treatment [10–12]. Among all the machine learning-based tools, ANNs have been widely applied in the context of SAHS and merit a thorough analysis.

In order to analyze the usefulness of ANNs in the management of SAHS, an exhaustive review of the studies published during the past decade has been carried out. The review is structured as follows. First, the most relevant tasks regarding the ANNs learning process are outlined in Section 2. In this regard, some user-dependent decisions involving the ANN design and major issues concerning the training and testing processes are detailed. Second, in Section 3, the most relevant applications of ANNs are analyzed, including automated diagnosis, sleep staging, and treatment monitoring.

#### **2. Artificial neural networks**

**1. Introduction**

34 Sleep Apnea - Recent Updates

clearly shows this trend.

In their daily practice, physicians must frequently decide a definitive diagnosis or the most suitable treatment using several variables from multiple clinical data sources, which is a highly complex task. A huge amount of valuable healthcare-related information is currently available, from symptoms reported by the patient and details stored in their clinical history to biochemical data and outcomes from biomedical recordings or medical images. In this context, machine learning methods are essential to maximize the usefulness of medical data in order to expedite decisions and avoid misdiagnosis. In the last decades, the increasing development of computers and artificial intelligence has led to the use of decision support expert systems in the common clinical practice of several fields of medicine [1, 2]. The huge number of studies published in the context of biomedical engineering during the last years

Bayesian theory was one of the first mathematical frameworks used to implement decision support systems. Regarding the classification of an item, according to the Bayes' decision rule, the predicted class must be the one that maximizes *a posteriori* probability in order to minimize the classification error. A major goal is to model the statistical characteristics of the problem under study, leading to expert systems able to assist physicians in decision-making processes. Among pattern recognition algorithms, conventional statistical classifiers, such as discriminant analysis [3] or logistic regression (LR) [4], and more recently artificial neural networks (ANNs) [5], have been widely applied. The widely known statistical classifiers assume that the class density function of input data is known *a priori*. Assumptions such as normal distribution, homoscedasticity, linearity, independency, or stationarity decrease the complexity of the classifier, minimize the classification error, and improve the performance. Nevertheless, these assumptions are not always consistent in real-world pattern classification problems, especially when working with limited datasets. Conversely, when using ANNs, no assumptions are made about the probability density functions of input features and the training data is used directly to optimize the decision rule [6]. Nevertheless, ANNs are characterized by a complex design stage. Both statistical and ANNs approaches have its advantages and limitations. However, the ability to model complex nonlinear problems, which are very common in biological systems, have made ANNs widely used in medical applications.

The first attempt to model information processing in biological systems by means of ANNs was carried out by McCulloch and Pitts in 1943 [7]. Since then, ANN-based algorithms have significantly evolved and their use in the field of medicine has increased considerably, particularly since the late 1990s. Some computer programs in the context of statistical medicine already include ANNs among their functionalities, which has contributed to increase their use in medical research. Nevertheless, "neural network" remains frequently a confusing term for many healthcare-related researchers. The implementation of an ANN has to be carried out by means of advanced software and some expertise is required to set up properly the userdependent input parameters. However, once designed, they are reliable and easy to use tools, even by nontrained personnel. In addition, once optimized, the computational time is small,

which is a major feature in order to speed up decision making.

ANNs are mathematical models inspired in the information processing capabilities of the nervous system designed to accomplish a predetermined task specified by the user [13, 14]. They were built to implement useful brain functions into a pattern recognition algorithm, such as parallel processing, distributed memory/storage, and environmental flexibility. ANNs are characterized by a fast and effective processing, learned from a preceding training process. During the learning or training stage, a wide set of known representative samples are used in order to model the statistical properties of the problem under study and accordingly compose the structure of the network. **Figure 1** illustrates a common network architecture of interconnected nodes arranged in layers simulating the brain's neuronal synapses.

The following advantages can be obtained when ANNs are applied for pattern recognition problems: (i) no prior assumptions about the data distribution are made as ANNs adjust themselves to the particular problem constrains during the learning process [15], (ii) ANNs are universal estimators able to match any function with arbitrary accuracy [16], and (iii) they are nonlinear algorithms able to model real-world complex relationships [15].

There are two major classes of ANNs: feedforward multilayered networks and radial basis function (RBF) networks. Both types of ANNs are capable of approximating any continuous functional mapping by means of several units (neurons) arranged in different layers [17]. The

**Figure 1.** Common network architecture of interconnected nodes arranged in three layers (input, hidden, and output) simulating the brain's neuronal synapses.

main difference is the way hidden units are activated, i.e., how the input data is used to compute the output of each unit. In feedforward ANNs, there is a fixed (usually nonlinear) activation function, whereas in RBF ANNs, the activation of each unit depends on the radial distance (typically Euclidean) between the input vector and a prototype vector (center) [18].

The multilayer perceptron (MLP) is the most widely used feedforward ANN in computer-aided medical research. Indeed, feedforward networks, particularly MLP, are the most popular ANN in the framework of SAHS management [19–21]. A particular implementation of MLP networks involving Bayesian inference during the learning process (BY-MLP), which increase the generalization ability and allow for relevance analysis of input variables, has demonstrated to be useful in this context [22]. Similarly, probabilistic neural networks (PNN), which also integrates the Bayes' theory into the learning process, have been recently applied in the SAHS diagnosis problem [23]. In addition, RBF ANNs [24, 25], such as learning vector quantization (LVQ), which is a precursor of self-organizing maps using the Hebbian learning-based approach [26, 27]; fuzzy neural networks (FNN), which incorporate the fuzzy inference system (FIS) into the learning process [26, 28, 29]; self-organizing maps (SOM) and adaptive resonance theory (ART) models, which are likely the most common unsupervised ANNs [30, 31]; and recurrent neural networks (RNN), which allow for closed-loop connections between units (feedback) [32], have been also applied in the framework of automated SAHS management.

Next, an overview of the conventional multilayered network architecture is provided, as well as the most important issues regarding the design, training, and validation stages common to all approaches in the ANN-based framework. **Figure 2** shows a flow diagram summarizing these stages.

#### **2.1. Network design: architecture of a neural network**

The so-called neuron is the basic element within an ANN, which comprises its elementary mathematical functions [17]. ANNs are composed of multiple interconnected nodes arranged in different levels or layers leading to a massive parallel structure. The first level is called the Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome http://dx.doi.org/10.5772/66570 37

main difference is the way hidden units are activated, i.e., how the input data is used to compute the output of each unit. In feedforward ANNs, there is a fixed (usually nonlinear) activation function, whereas in RBF ANNs, the activation of each unit depends on the radial distance

**Figure 1.** Common network architecture of interconnected nodes arranged in three layers (input, hidden, and output)

The multilayer perceptron (MLP) is the most widely used feedforward ANN in computer-aided medical research. Indeed, feedforward networks, particularly MLP, are the most popular ANN in the framework of SAHS management [19–21]. A particular implementation of MLP networks involving Bayesian inference during the learning process (BY-MLP), which increase the generalization ability and allow for relevance analysis of input variables, has demonstrated to be useful in this context [22]. Similarly, probabilistic neural networks (PNN), which also integrates the Bayes' theory into the learning process, have been recently applied in the SAHS diagnosis problem [23]. In addition, RBF ANNs [24, 25], such as learning vector quantization (LVQ), which is a precursor of self-organizing maps using the Hebbian learning-based approach [26, 27]; fuzzy neural networks (FNN), which incorporate the fuzzy inference system (FIS) into the learning process [26, 28, 29]; self-organizing maps (SOM) and adaptive resonance theory (ART) models, which are likely the most common unsupervised ANNs [30, 31]; and recurrent neural networks (RNN), which allow for closed-loop connections between units (feedback) [32], have been also

Next, an overview of the conventional multilayered network architecture is provided, as well as the most important issues regarding the design, training, and validation stages common to all approaches

The so-called neuron is the basic element within an ANN, which comprises its elementary mathematical functions [17]. ANNs are composed of multiple interconnected nodes arranged in different levels or layers leading to a massive parallel structure. The first level is called the

in the ANN-based framework. **Figure 2** shows a flow diagram summarizing these stages.

(typically Euclidean) between the input vector and a prototype vector (center) [18].

applied in the framework of automated SAHS management.

simulating the brain's neuronal synapses.

36 Sleep Apnea - Recent Updates

**2.1. Network design: architecture of a neural network**

**Figure 2.** Flow diagram summarizing the training, validation, and test stages, as well as the most important issues involved in the design of an ANN.

input layer. Neurons in the input layer process directly the feature vectors or patterns that feed the ANN. Similarly, each output from every neuron in a layer feeds neurons composing the subsequent layer, leading to a distributed complex structure. The last level of the network, whose nodes provide the output of the ANN, is called the output layer. The remaining internal levels are called hidden layers. Both the number of hidden layers and the number of nodes are flexible and are determined during the learning process. The feedforward architecture is the most widely used, where each neuron in a layer is connected to every neuron on the next layer but neither connections between units in the same level nor closed-loops (feedback) are allowed. Therefore, data is always moving forward from one layer to the next, i.e., from the input to the output.

There is not a predetermined network architecture known to be *a priori* the best for any problem under study in terms of performance. The mathematical operation accomplished by each neuron is always the same. Therefore, the functionality of the ANN, i.e., the way in which a particular problem is addressed, is determined by the strength of the link between each pair of neurons. This strength is characterized by the coefficients of the ANN, the so-called weights, which are optimized during the training stage. Similar to the process of memory, weights represent the information stored in the network, whereas the optimization procedure represents the learning process or statistical inference [18].

As aforementioned, the structure of an ANN depends on the number of hidden layers, the number of neurons per layer, and the connectivity strength among them. Regarding the number of levels, it is common to construct ANNs with a single hidden layer because it has been demonstrated that this architecture is able to achieve universal approximation [33]. This is a userdependent decision, whereas the number of neurons and the connectivity degree (weights) are both determined automatically during the learning process. Regarding the number of nodes in the hidden layer, it is commonly optimized by means of a hold-out or cross-validation approach using the data in a training dataset. In this regard, it is supposed that the complex the problem, the higher the number of neurons. Notwithstanding, even a small network with a reduced number of nodes can model complex problems and reach high prediction ability. In addition, the following design issues must be addressed before the learning process [17]: the output coding scheme, the error function used in the network training, and the activation function of neurons in the hidden and output layers. The hyperbolic tangent function is a common activation function for neurons in the hidden layer since it has been demonstrated that it provides fast convergence of training algorithms [13, 17]. **Figure 3** shows a common schema of a single neuron (perceptron) with a sigmoid activation function. Regarding the learning process, the scale conjugate gradient (SCG) is a common method for updating the adjustable parameters of the ANN (weights and biases) during the training stage.

#### *2.1.1. Classification and regression approaches*

According to the mathematical nature of the output, ANNs can be applied to address two main kinds of problems: classification and regression. Regarding the classification approach, the goal of the ANN is to estimate the class membership for an input feature pattern among a

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome http://dx.doi.org/10.5772/66570 39

input layer. Neurons in the input layer process directly the feature vectors or patterns that feed the ANN. Similarly, each output from every neuron in a layer feeds neurons composing the subsequent layer, leading to a distributed complex structure. The last level of the network, whose nodes provide the output of the ANN, is called the output layer. The remaining internal levels are called hidden layers. Both the number of hidden layers and the number of nodes are flexible and are determined during the learning process. The feedforward architecture is the most widely used, where each neuron in a layer is connected to every neuron on the next layer but neither connections between units in the same level nor closed-loops (feedback) are allowed. Therefore, data is always moving forward from one layer to the next, i.e., from the

There is not a predetermined network architecture known to be *a priori* the best for any problem under study in terms of performance. The mathematical operation accomplished by each neuron is always the same. Therefore, the functionality of the ANN, i.e., the way in which a particular problem is addressed, is determined by the strength of the link between each pair of neurons. This strength is characterized by the coefficients of the ANN, the so-called weights, which are optimized during the training stage. Similar to the process of memory, weights represent the information stored in the network, whereas the optimization procedure