3.1.5. *[(1E,6E)-4,4-bis(Methoxymethyl)-3,5-dioxohepta-1,6-diene-1,7-diyl]bis(2-methoxy-4,1-phenylene) bis[3-hydroxy-2-(hydroxymethyl)-2-methylpropanoate]* (**11**)

To a stirred solution of crude **8** (0.453 g) in DMF (1.0 mL) were added K2CO3 (0.240 g, 1.73 mmol) and MeI (0.247 g, 1.73 mmol) at 0 oC. The reaction mixture was warmed to room temperature and stirred for 6 h. Then H2O (10 mL) and diluted HCl(aq) (1 *N*, 3 mL) were added to quench the reaction. The aqueous layer was separated and extracted with EtOAc (3 × 8 mL). The combined organic extracts were washed with brine, dried over MgSO4, filtered and concentrated to give the crude product, which was then treated with HCl(aq) (6 *N*, 0.25 mL) in MeOH (2 mL) at room temperature. The reaction mixture was stirred at room temperature for 1h and then concentrated under vacuum to give the crude product, which was purified on silica gel with MeOH/CH2Cl2 (1:20) to afford **11** (73 mg) as a yellow solid (mp = 187–188 ◦C). Yield 5% for three steps; 1H-NMR (DMSO-d6) δ 7.65 (d, *J* = 16.0 Hz, 2H), 7.49 (s, 2 H), 7.31 (d, *J* = 10.0 Hz, 2H), 7.08 (d, *J* = 8.1 Hz, 2H), 6.69 (d, *J* = 16.0 Hz, 2H), 4.85 (t, *J* = 5.5 Hz, 4 H), 3.80 (s, 6H), 3.74 (s, 6 H), 3.66-3.60 (m, 8H), 1.20 (s, 6H); 13C-NMR (DMSO-d6) δ 193.1, 173.4, 167.1, 151.6, 144.4, 141.8, 133.2, 123.8, 121,9, 118.4, 112.6, 64.0, 56.3, 51.9, 51.0, 17.3; HRMS [ESI]<sup>+</sup> calculated for C35H44NaO14: 711.2629 [M + Na]+; found: 711.2633.


**7. HPV E6/E7 mRNA screening methods**

Several recent studies have clearly shown that testing for HPV mRNA instead of HPV DNA can be clinically useful [70, 93, 94]. The most relevant transcripts for diagnostic purposes are those encoding viral oncoproteins E6 and E7. The detection of viral mRNA can be done by reverse

transcriptase real time PCR [95] or by nucleic acid sequence-based amplification (NASBA). For the latter, three commercially available assays that detect E6/E7 transcripts are currently available. PreTect HPV-Proofer (HPV-Proofer; NorChip, Klokkarstua, Norway) is an assay based on NASBA technology, which allows qualitative determination of E6/E7 mRNA tran‐ scriptsofthefivemostfrequentlyidentifiedhr-HPVtypesincervicalcancerworldwide:HPV-16, HPV-18, HPV-31, HPV-33 and HPV-45 [96]. The NucliSENS EasyQ HPV V1 assay (NucliS‐ ENS; bioMérieux) was launched in 2007 and is based on the original HPV-Proofer assay, except for the proprietary hardware platform and the software for NASBA measurements and data analysis[97].APTIMAHPVAssay(APTIMA;Gen-Probe,SanDiego,CA,USA)isatranscriptionmediated amplification-based assay, which allows the detection of E6/E7 mRNA transcripts of 14HPVtypes:HPV-16,HPV-18,HPV-31,HPV-33,HPV-35,HPV-39,HPV-45,HPV-51,HPV-52, HPV-56, HPV-58, HPV-59, HPV-66 and HPV-68. The assay generates a qualitative result for the presence/absenceof14targetedHPVsanddoesnotallowtheexactdeterminationofHPVtype(s) presentinaclinical specimen[98].APTIMAyieldedsimilar sensitivityforCIN2+comparedwith hc2,Amplicor andLinearArray(95.2vs 99.6%, 98.9%and98.2%,respectively),but a significant‐ ly higher specificity (42.2 vs 28.4%, 21.7% and 32.8%, respectively) [55]. In a comparative evaluation of APTIMA and hc2 on PreservCyt specimens collected from 800 women referred to colposcopy, APTIMA showed comparable sensitivity to hc2 for the detection of CIN2+ (91 vs 95%), as well as CIN3+ (98 vs 99%), but had higher clinical specificity (>55 vs 47% for CIN2+; 53 vs 44% for CIN3+) [99]. APTIMA had the best sensitivity/specificity balance measured by AUC (areaunderROCcurve) comparisontest(significantforCIN2+),andthe colposcopyreferralrate (9.2%) comparable to that of liquid citology (8.7%) [100].

cervical surface and (ii) the level of viral production in the area of infection. Viral load has been suggested to be a potential biomarker for cervical intraepithelial neoplasia grade 2 (CIN2) or greater, but currently there is no consistent evidence that a one-time measurement of viral load is a useful marker of prevalent disease or disease progression [101]. A widespread productive infection might be associated with high viral load, while a small incipient CIN3 with low-level virus production might be associated with low viral load. Furthermore, viral load in a cytological sample is subject to sampling variation in which there are varying proportions of lesional cells, normal epithelial cells, inflammatory exudate, and blood. A further complication in using viral load to predict neoplasia of CIN2 or greater is the high prevalence of multiple carcinogenic HPV infections detected in cervical samples. The current paradigm is that cervical lesions clonally expand following infection with one specific genotype (one virus-one lesion concept). On the cervical surface, multiple independent infections or lesions may occur that are caused by different genotypes. Without specific genotyping conducted *in situ*, assigning a causal HPV genotype to a specific lesion can only be based on assumptions [102]. HPV16 is the only genotype for which there is some indication that viral load may predict viral persis‐

HPV Diagnosis in Vaccination Era http://dx.doi.org/10.5772/55818 69

Quantitating HPV viral load seems to be a rational strategy of identifying women at risk for persistent HPV infection and progression to high-grade dysplasia. Accordingly, a high viral load could represent many cells with few virions each or a few cells containing many virions. An inaccurate description of the viral biology and the possible implications for the host could result from this discrepancy. Recently, studies have focused on longitudinal observations of viral load to predict viral clearance or lesion progression [106, 107]. Initial data indicate that repeated measurements can improve prediction of persistence or clearance, but these data are, so far, limited to HPV16 only. Although signal intensities from HC2 or various endpoint PCRbased assays have been proposed and partly used as surrogates for viral load, these approaches have limitations [108, 109]. HC2 only gives aggregate signal strength for a pool of 13 carcino‐ genic types, and commercial genotyping assays, such as the Roche Linear Array (LA) or Innogenetics InnoLiPA (line probe assay), do not formally report quantitative results. Over‐ estimation of the presence of oncogenic HPV may result. Despite these caveats, the develop‐ ment of HPV viral load assays that may reliably be used as an adjunct screening tool to identify women at increased risk of progression to CIN 2+ and cervical cancer remains a promising tool in cervical cancer screening. Recently, Wentzensen et al [110] measuring signal intensities on LA HPV genotyping strips provides quantitative information comparable to viral load measurements based on Q-PCR. This approach offers the potential for viral load assessment for 37 types in parallel, simplifying conducting repeated measurements of viral load in epidemiologic studies and addressing the problems of multiple HPV genotype infections in

Screening for HPV integration into the host genome is a subcategory of HPV diagnostics. HPV integration is a key molecular event in the transition from an innocuous HPV infection to one that has oncogenic potential. Human papilloma virus integration results in increased expres‐ sion of the viral E6 and E7 proteins. Increased expression of these proteins ultimately results in the disruption of host cell proteins, p53 and retinoblastoma protein [111]. Tests that detect

tence and progression to precancer [103-105].

studies of HPV load.


**Abstract**

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.

**Keywords:** machine learning, wearable medical devices, real-time detection, sensors for wearable devices

## **1. Introduction**

Healthcare is one of the most crucial application areas that require faster development. In the wearable medical device domain, there is a lot of scope of research and development. With the recent technological development, wearable medical devices become feasible for real-time monitoring and diagnosis of medical problems. It has been accelerated with the availability of cheaper component and advancement in wireless communication technology, which has also enabled the application of the Internet of Things (IoT) in the wearable biomedical applications. According to a report by Grand View Research Inc. [1], the Internet of Things (IoT) penetration in the health sector is expected to reach a volume of approximately \$ 409.9 billion by 2022. Thus, there is a huge boom in the wearable industry. There is a wide range of wearable devices that transmit medical information to mobile and

web applications over the wireless network, enabling remote health monitoring. The wearable medical devices face the challenges of reliability, accuracy, precision, and robustness [2]. There are two important sub-sections for a complete wearable medical device—(i) hardware, which involves the sensor selection and characterization, noise removal from the acquired signals, and communication to the decisionmaking subsystem and (ii) software, which involves taking decisions based on the acquired signals. The second subsystem is where the machine learning comes into play, and the recent outburst of development in the field of machine learning has increased the possibility of remote monitoring and diagnosing with the acquired data from the wearable devices. The machine learning block of the wearable devices involves various steps such as preprocessing, feature selection, training on the labeled dataset, and testing to verify its accuracy and competency. The various parts of the machine learning block will be discussed in details later in this chapter. Thus, it is inevitable that machine learning methods that include algorithm training are required to enable the diagnosis of different diseases. Examples of success in machine learning lead researchers to the adoption of neural networks and other classifiers for medical diagnostic applications. However, this requires ground truth in the form of accurately curated data sources from carefully constructed subject trials with a properly distributed subject population of significant size. Limitations in ground truth data may lead to inaccurate results from the classifiers. This also includes a novel set of challenges for the design and execution of subject trials that are essential in the development and training of algorithms, as well as verification of system performance. Different open-source databases were used for hardware evaluation. The chapter is intended to provide a guidance to the researchers in computing for healthcare who wish to adopt the compelling mission of wearable device development for healthcare diagnostics.

The chapter draws from the experience in the development of a series of biomedical devices from primary research done in the field of wearable devices for monitoring heart attack, abnormal heart sounds, gait analysis in diabetic foot monitoring, and blood pressure estimation using photoplethysmogram (PPG) signal. Therefore, in this chapter, four major health issues were discussed and how these issues can be well monitored without visiting hospital facilities were discussed. These experiments from various previously performed case studies by the team can help in providing a step-by-step roadmap on how to develop intelligent wearable biomedical system. Section 2 discusses the sensor selection and characterization process for a smart insole for gait analysis in diabetic foot monitoring. Section 3 discusses a case study, how continuous blood pressure can be estimated very accurately using PPG signal with the help of machine learning algorithm. Section 4 shows a wearable device for heart attack detection for the driver in a vehicle environment using classical machine learning approach, while Section 5 describes a wearable monitoring device, which is developed for heart sound monitoring to identify cardiovascular abnormality. Section 6 discusses the key contributions of the chapter along with the major results and their applications in real world. Finally, Section 7 concludes the chapter.

## **2. Smart insole for gait analysis**

Gait analysis is an important feature for diabetic foot monitoring. A force between the foot and ground, called the vertical ground reaction force (vGRF), can be acquired using different wearable sensors such as force sensors, strain gauges, magneto-resistive sensors, accelerometers, gyroscopes, and inclinometers or using different type of instruments such as force plate system and

**75**

**Figure 1.**

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

Analysis Lab as a calibration reference.

*Block diagram for a diabetic foot monitoring smart foot sole.*

multi-camera-based system. Despite the improvements in technology, the cost of the multi-camera setup is high and it requires a long post-processing time. Furthermore, the number of strides that can be captured is limited, making it not applicable for personal use. Some efforts from different research groups have been made to overcome the limitations of traditional force plates by equipping treadmills with few force plates [3]. However, treadmills impose restrictions as subjects cannot change their direction of walking. Therefore, an increase in research interest was observed toward the development of smart insoles, where wearable sensors can be employed to detect vGRF, joint movements, acceleration of lower extremities, and other gait variables [4]. Smart insole provides the researchers with a flexible, portable, and comfortable solution for vGRF measurement. It includes different features including monitoring, processing, and displaying plantar pressure using pressure sensors embedded in the insole. **Figure 1** shows the complete block diagram of such a wearable system, where the pressure sensor array is placed in a customized shoe above the control circuit. Pressure data have to be digitized in the insole subsystem before it is send via a Bluetooth to a host computer for post-processing and analysis. This subsystem is powered by a battery alongside a power management unit. Pressure data can then be analyzed to extract various gait characteristics for different gait applications. In this section, the design of smart soles to detect vGRF during gait, with two different types of low-cost commercial force sensor: FSRs and flexible piezoelectric sensors (**Figure 2**), is discussed. All the sensors can be calibrated, details of which can be found in [5], where a simple low-cost calibration method based on load cells has been shown to mitigate the need of expensive calibration devices or Motion

As shown in **Figure 3**, the most common places of foot plane where most of the pressure is exerted during gait are heal, metatarsal heads, hallux, and toe. These are

In each insole, 16 sensors were positioned to record pressure values in different locations of the foot sole as shown in **Figure 3b**. Since people typically do not exert pressure on the Medial arch area, there is no sensor placed in this area [8]. Sixteen FSRs/piezoelectric sensors were used to collect data from smart insole, as can be seen in **Figures 4** and **5**. Sixteen inputs were multiplexed to one output through a 16-to-1 multiplexer, then applied to an ADC input of the microcontroller, and sent to host computer. The acquisition circuit was connected to the microcontroller module using an insulated ribble cable to minimize wire path and electrical hazard. The instrumented insole was placed on the top of the insole of the user's shoe to acquire pressure data while the other end of the ribbon cable is connected to a small box with an adhesive strap to wrap around the leg. Real-time pressure data from different sensors were collected in the microcontroller and sent to a PC using

the places where the calibrated sensors are to be placed on the smart insole.

#### *Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

*Sports Science and Human Health - Different Approaches*

ment for healthcare diagnostics.

concludes the chapter.

**2. Smart insole for gait analysis**

web applications over the wireless network, enabling remote health monitoring. The wearable medical devices face the challenges of reliability, accuracy, precision, and robustness [2]. There are two important sub-sections for a complete wearable medical device—(i) hardware, which involves the sensor selection and characterization, noise removal from the acquired signals, and communication to the decisionmaking subsystem and (ii) software, which involves taking decisions based on the acquired signals. The second subsystem is where the machine learning comes into play, and the recent outburst of development in the field of machine learning has increased the possibility of remote monitoring and diagnosing with the acquired data from the wearable devices. The machine learning block of the wearable devices involves various steps such as preprocessing, feature selection, training on the labeled dataset, and testing to verify its accuracy and competency. The various parts of the machine learning block will be discussed in details later in this chapter. Thus, it is inevitable that machine learning methods that include algorithm training are required to enable the diagnosis of different diseases. Examples of success in machine learning lead researchers to the adoption of neural networks and other classifiers for medical diagnostic applications. However, this requires ground truth in the form of accurately curated data sources from carefully constructed subject trials with a properly distributed subject population of significant size. Limitations in ground truth data may lead to inaccurate results from the classifiers. This also includes a novel set of challenges for the design and execution of subject trials that are essential in the development and training of algorithms, as well as verification of system performance. Different open-source databases were used for hardware evaluation. The chapter is intended to provide a guidance to the researchers in computing for healthcare who wish to adopt the compelling mission of wearable device develop-

The chapter draws from the experience in the development of a series of biomedical devices from primary research done in the field of wearable devices for monitoring heart attack, abnormal heart sounds, gait analysis in diabetic foot monitoring, and blood pressure estimation using photoplethysmogram (PPG) signal. Therefore, in this chapter, four major health issues were discussed and how these issues can be well monitored without visiting hospital facilities were discussed. These experiments from various previously performed case studies by the team can help in providing a step-by-step roadmap on how to develop intelligent wearable biomedical system. Section 2 discusses the sensor selection and characterization process for a smart insole for gait analysis in diabetic foot monitoring. Section 3 discusses a case study, how continuous blood pressure can be estimated very accurately using PPG signal with the help of machine learning algorithm. Section 4 shows a wearable device for heart attack detection for the driver in a vehicle environment using classical machine learning approach, while Section 5 describes a wearable monitoring device, which is developed for heart sound monitoring to identify cardiovascular abnormality. Section 6 discusses the key contributions of the chapter along with the major results and their applications in real world. Finally, Section 7

Gait analysis is an important feature for diabetic foot monitoring. A force between the foot and ground, called the vertical ground reaction force (vGRF), can be acquired using different wearable sensors such as force sensors, strain gauges, magneto-resistive sensors, accelerometers, gyroscopes, and inclinometers or using different type of instruments such as force plate system and

**74**

multi-camera-based system. Despite the improvements in technology, the cost of the multi-camera setup is high and it requires a long post-processing time. Furthermore, the number of strides that can be captured is limited, making it not applicable for personal use. Some efforts from different research groups have been made to overcome the limitations of traditional force plates by equipping treadmills with few force plates [3]. However, treadmills impose restrictions as subjects cannot change their direction of walking. Therefore, an increase in research interest was observed toward the development of smart insoles, where wearable sensors can be employed to detect vGRF, joint movements, acceleration of lower extremities, and other gait variables [4]. Smart insole provides the researchers with a flexible, portable, and comfortable solution for vGRF measurement. It includes different features including monitoring, processing, and displaying plantar pressure using pressure sensors embedded in the insole.

**Figure 1** shows the complete block diagram of such a wearable system, where the pressure sensor array is placed in a customized shoe above the control circuit. Pressure data have to be digitized in the insole subsystem before it is send via a Bluetooth to a host computer for post-processing and analysis. This subsystem is powered by a battery alongside a power management unit. Pressure data can then be analyzed to extract various gait characteristics for different gait applications.

In this section, the design of smart soles to detect vGRF during gait, with two different types of low-cost commercial force sensor: FSRs and flexible piezoelectric sensors (**Figure 2**), is discussed. All the sensors can be calibrated, details of which can be found in [5], where a simple low-cost calibration method based on load cells has been shown to mitigate the need of expensive calibration devices or Motion Analysis Lab as a calibration reference.

As shown in **Figure 3**, the most common places of foot plane where most of the pressure is exerted during gait are heal, metatarsal heads, hallux, and toe. These are the places where the calibrated sensors are to be placed on the smart insole.

In each insole, 16 sensors were positioned to record pressure values in different locations of the foot sole as shown in **Figure 3b**. Since people typically do not exert pressure on the Medial arch area, there is no sensor placed in this area [8]. Sixteen FSRs/piezoelectric sensors were used to collect data from smart insole, as can be seen in **Figures 4** and **5**. Sixteen inputs were multiplexed to one output through a 16-to-1 multiplexer, then applied to an ADC input of the microcontroller, and sent to host computer. The acquisition circuit was connected to the microcontroller module using an insulated ribble cable to minimize wire path and electrical hazard. The instrumented insole was placed on the top of the insole of the user's shoe to acquire pressure data while the other end of the ribbon cable is connected to a small box with an adhesive strap to wrap around the leg. Real-time pressure data from different sensors were collected in the microcontroller and sent to a PC using

**Figure 1.** *Block diagram for a diabetic foot monitoring smart foot sole.*

#### **Figure 2.**

*(a) FSR sensor (Interlink Electronics) [6] and (b) piezoelectric sensor (Murata Manufacturing Co.) [7].*

#### **Figure 3.**

*Selected foot area for pressure acquiring (a) and array of pressure sensor (b).*

**Figure 4.** *FSR-based smart insole: top (a) and bottom (b).*

Bluetooth low energy interface, and in the PC, a graphical interface was used to display and analyze the signal.

A commercial smart insole system called F-scan, which is one of the best insoles currently available in the market, was used to validate the performance of the designed smart insole (**Figure 6a**). The F-scan system is designed with very thin flexible printed circuit board with 960 sensing points, and each point can measure pressure with eight-bit resolution with a sampling frequency of maximum 750 Hz. However, the F-scan system is very expensive; the wired system costs around \$13,000, while the wireless systems costs around \$17,000. On the other hand, the proposed instrumented insole costs only ~500\$. Typically the vGRF peak is only 10% of the subject's weight. So, the data were calibrated based on the subject's weight. The subject is supposed to stand on one foot for few seconds while wearing the smart insole, and the pressure is calculated for the purpose of calibration. If the subject's weight is higher than the pressure measured, the calibration was done by a calibration factor greater than 1 while the calibration factor is less than 1 for

**77**

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

**Figure 5.**

**Figure 6.**

*system (c).*

**Figure 7.**

*comfortability.*

patient's weight less than the applied pressure. Calibration factor was calculated in the similar way for the FSR insole. **Figure 6b** and **c** show the application of F-scan and FSR-based system on the same subject while acquiring the vGRF signal. The FSR-based smart insole can acquire high-quality vGRF for different gait cycles, as seen in **Figure 7**, and it was found that the flexible piezoelectric sensors were performing poor in calibration due to their sensitivity to 3D forces requiring special force calibration machines to control the applied force in x, y, or z directions. Therefore, piezoelectric sensors cannot be utilized as a substitute for FSR in designing smart insole. However, the piezoelectric sensors if calibrated properly can be used to

*Comparison between the mean and standard deviation of vertical ground reaction forces (vGRFs) from left*

*(blue) and right (orange) foot using F-scan system (a) and FSR-based system (b).*

*(a) Piezo insole with 16 piezo sensors and (b) additional insole layer placed on the top of piezo insole to ensure*

*Commercial F-scan smart insole (a), subject 01 is wearing the F-scan system (b), and FSR-based prototype*

**3. Estimating blood pressure from the photoplethysmogram signal**

It is very important to monitor the blood pressure as high blood pressure puts a huge amount of strain on the arteries and the heart, resulting in increase in the

detect the starting and ending of each gait cycle.

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

**Figure 5.**

*Sports Science and Human Health - Different Approaches*

**76**

**Figure 4.**

**Figure 3.**

**Figure 2.**

*FSR-based smart insole: top (a) and bottom (b).*

*Selected foot area for pressure acquiring (a) and array of pressure sensor (b).*

display and analyze the signal.

Bluetooth low energy interface, and in the PC, a graphical interface was used to

*(a) FSR sensor (Interlink Electronics) [6] and (b) piezoelectric sensor (Murata Manufacturing Co.) [7].*

currently available in the market, was used to validate the performance of the designed smart insole (**Figure 6a**). The F-scan system is designed with very thin flexible printed circuit board with 960 sensing points, and each point can measure pressure with eight-bit resolution with a sampling frequency of maximum 750 Hz. However, the F-scan system is very expensive; the wired system costs around \$13,000, while the wireless systems costs around \$17,000. On the other hand, the proposed instrumented insole costs only ~500\$. Typically the vGRF peak is only 10% of the subject's weight. So, the data were calibrated based on the subject's weight. The subject is supposed to stand on one foot for few seconds while wearing the smart insole, and the pressure is calculated for the purpose of calibration. If the subject's weight is higher than the pressure measured, the calibration was done by a calibration factor greater than 1 while the calibration factor is less than 1 for

A commercial smart insole system called F-scan, which is one of the best insoles

*(a) Piezo insole with 16 piezo sensors and (b) additional insole layer placed on the top of piezo insole to ensure comfortability.*

#### **Figure 6.**

*Commercial F-scan smart insole (a), subject 01 is wearing the F-scan system (b), and FSR-based prototype system (c).*

#### **Figure 7.**

*Comparison between the mean and standard deviation of vertical ground reaction forces (vGRFs) from left (blue) and right (orange) foot using F-scan system (a) and FSR-based system (b).*

patient's weight less than the applied pressure. Calibration factor was calculated in the similar way for the FSR insole. **Figure 6b** and **c** show the application of F-scan and FSR-based system on the same subject while acquiring the vGRF signal.

The FSR-based smart insole can acquire high-quality vGRF for different gait cycles, as seen in **Figure 7**, and it was found that the flexible piezoelectric sensors were performing poor in calibration due to their sensitivity to 3D forces requiring special force calibration machines to control the applied force in x, y, or z directions. Therefore, piezoelectric sensors cannot be utilized as a substitute for FSR in designing smart insole. However, the piezoelectric sensors if calibrated properly can be used to detect the starting and ending of each gait cycle.

#### **3. Estimating blood pressure from the photoplethysmogram signal**

It is very important to monitor the blood pressure as high blood pressure puts a huge amount of strain on the arteries and the heart, resulting in increase in the

probability of clogging of lumens. This clot (clogging of lumens) may cause several health problems such as heart attack, stroke, kidney diseases, and dementia. Thus, continuous blood pressure monitoring, at least four times daily at regular intervals, is preferable. Photoplethysmography (PPG) can be used for measuring the amount of light absorbed or reflected by blood vessels in the living tissue which can be extended to different aspects of cardiovascular surveillance including identification of blood oxygen saturation, heart rate, BP estimation, cardiac output, respiration, arterial aging, endothelial control, microvascular blood flow, and autonomic function. PPG waveforms generally have three distinct features—systolic peak, diastolic peak, and a notch in between—as shown in **Figure 8**.

This section will discuss how the less cumbersome method of PPG signal acquisition can be used in estimating blood pressure, details of which can be found in [9]. This section will also discuss how a trained network using reliable labeled dataset can be used in such a study.

#### **3.1 Dataset**

The dataset used in this study was taken from Liang et al. [10], which is publicly available. The dataset contained 657 PPG signal samples from 219 subjects. PPG signals were first assessed to check the signal quality and then randomly divided into two sets. Then, 85% of the data were used for training and validation, and 15% of the data were used for testing the performance of the model. The PPG signals were preprocessed before they were sent for feature extraction. After extracting meaningful features, feature selection techniques were used to reduce computational complexity and the chance of over-fitting the algorithm. The features were then used to train machine learning algorithms. The best regression model was selected for SBP and DBP estimation individually. In the quality assurance process, 222 signals from 126 subjects were finally kept for this study. **Figure 9** shows the sample PPG signal which was divided into fit and unfit for the study.

#### **3.2 Normalization and filtration**

To extract meaningful information from the signals, it was necessary to normalize all the signals. The Z-score technique was used to normalize the signals in this study to get amplitude-limited data. The normalized signal is passed into a low-pass Butterworth filter to remove the high frequency that is acquired by the signal.

#### **3.3 Baseline correction**

The PPG waveform is commonly contaminated with a baseline wandering due to respiration at frequencies ranging from 0.15 to 0.5 Hz. Therefore, it is very important that the signal is properly filtered to remove the baseline wandering but that important information is preserved as far as possible. A polynomial fit was used to find the trend in the signal and subtracted to get the baseline corrected signal, as shown in **Figure 10**. The next step is to extract features from the preprocessed signal.

#### **3.4 Feature extraction**

The block diagram summarizing the feature extraction details adopted in the study is shown in **Figure 11**. A PPG waveform contains many informative information such as systole, diastole, notch, pulse width, and peak-to-peak interval. Some of the

**79**

**Figure 9.**

**Figure 8.**

distinctive features of the PPG waveform might not be dominant in some patients,

*Comparison of waveforms that are fit and unfit for the study. (a) Fit data and (b) unfit data.*

*A typical photoplethysmography (PPG) waveform with notch, systolic peak, and diastolic peak.*

Feature selection or reduction is important to reduce the risk of over-fitting the algorithms. In this section, three feature selection methods: correlation-based

such as the notch prevalence changing with age.

**3.5 Feature reduction**

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

#### **Figure 8.**

*Sports Science and Human Health - Different Approaches*

peak, and a notch in between—as shown in **Figure 8**.

can be used in such a study.

**3.2 Normalization and filtration**

**3.3 Baseline correction**

**3.4 Feature extraction**

**3.1 Dataset**

probability of clogging of lumens. This clot (clogging of lumens) may cause several health problems such as heart attack, stroke, kidney diseases, and dementia. Thus, continuous blood pressure monitoring, at least four times daily at regular intervals, is preferable. Photoplethysmography (PPG) can be used for measuring the amount of light absorbed or reflected by blood vessels in the living tissue which can be extended to different aspects of cardiovascular surveillance including identification of blood oxygen saturation, heart rate, BP estimation, cardiac output, respiration, arterial aging, endothelial control, microvascular blood flow, and autonomic function. PPG waveforms generally have three distinct features—systolic peak, diastolic

This section will discuss how the less cumbersome method of PPG signal acquisition can be used in estimating blood pressure, details of which can be found in [9]. This section will also discuss how a trained network using reliable labeled dataset

The dataset used in this study was taken from Liang et al. [10], which is publicly available. The dataset contained 657 PPG signal samples from 219 subjects. PPG signals were first assessed to check the signal quality and then randomly divided into two sets. Then, 85% of the data were used for training and validation, and 15% of the data were used for testing the performance of the model. The PPG signals were preprocessed before they were sent for feature extraction. After extracting meaningful features, feature selection techniques were used to reduce computational complexity and the chance of over-fitting the algorithm. The features were then used to train machine learning algorithms. The best regression model was selected for SBP and DBP estimation individually. In the quality assurance process, 222 signals from 126 subjects were finally kept for this study. **Figure 9** shows the

sample PPG signal which was divided into fit and unfit for the study.

To extract meaningful information from the signals, it was necessary to normalize all the signals. The Z-score technique was used to normalize the signals in this study to get amplitude-limited data. The normalized signal is passed into a low-pass Butterworth filter to remove the high frequency that is acquired by the

The PPG waveform is commonly contaminated with a baseline wandering due to respiration at frequencies ranging from 0.15 to 0.5 Hz. Therefore, it is very important that the signal is properly filtered to remove the baseline wandering but that important information is preserved as far as possible. A polynomial fit was used to find the trend in the signal and subtracted to get the baseline corrected signal, as shown in **Figure 10**. The next step is to extract features from the preprocessed signal.

The block diagram summarizing the feature extraction details adopted in the study is shown in **Figure 11**. A PPG waveform contains many informative information such as systole, diastole, notch, pulse width, and peak-to-peak interval. Some of the

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signal.

*A typical photoplethysmography (PPG) waveform with notch, systolic peak, and diastolic peak.*

#### **Figure 9.**

*Comparison of waveforms that are fit and unfit for the study. (a) Fit data and (b) unfit data.*

distinctive features of the PPG waveform might not be dominant in some patients, such as the notch prevalence changing with age.

#### **3.5 Feature reduction**

Feature selection or reduction is important to reduce the risk of over-fitting the algorithms. In this section, three feature selection methods: correlation-based

#### **Figure 10.**

*Baseline correction of PPG waveform. (a) PPG waveform with the baseline wandering and fourth degree polynomial trend and (b) PPG waveform after detrending.*

#### **Figure 11.** *Overview of feature extraction.*

feature selection (CFS), ReliefF features selection, and features for classification using the minimum redundancy maximum relevance (fscmrmr) algorithm is discussed. ReliefF is a feature selection algorithm, which randomly selects instances and adjusts the weights of the respective element depending on the nearest neighbor. Correlation is a test used to evaluate whether or not a feature is highly correlated with the class or not highly correlated with any of the other features. On the other hand, the fscmrmr algorithm finds an optimal set of features that are mutually as dissimilar as possible and can effectively represent the response variable. The algorithm minimizes a feature set's inconsistency and maximizes the relevance of a feature set to the answer variable. MATLAB built-in functions can be used for CFS, ReliefF, and fscmrmr feature selection algorithms.

After the features were extracted, the feature matrix was trained with machine learning algorithms. The Regression Learner App of MATLAB 2019b was used to estimate the BP. Five different algorithms [linear regression, regression trees, support vector regression (SVR), Gaussian process regression (GPR), and ensemble trees] with their variations to a total of 19 algorithms were trained using the 10-fold

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**3.6 Results**

*pressure (DBP) after optimization.*

**Table 1.**

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

> **Performance criteria**

**Optimized GPR**

ReliefF MAE **3.02** 11.32 **1.74** 5.99

FSCMRMR MAE 6.11 14.65 6.80 8.22

CFS MAE 12.95 16.27 7.59 7.89

**Selection criteria**

Features from the literature

All features (newly designed and from the literature)

cross-validation. Out of all these algorithms, two best performing algorithms,

*Evaluation of the outperforming algorithms for estimating systolic blood pressure (SBP) and diastolic blood*

To evaluate the performance of the ML algorithms for estimating BP, it is important to select proper metrics, i.e., four in this case and are stated below.

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

#### **Table 1.**

*Sports Science and Human Health - Different Approaches*

*polynomial trend and (b) PPG waveform after detrending.*

feature selection (CFS), ReliefF features selection, and features for classification using the minimum redundancy maximum relevance (fscmrmr) algorithm is discussed. ReliefF is a feature selection algorithm, which randomly selects instances and adjusts the weights of the respective element depending on the nearest neighbor. Correlation is a test used to evaluate whether or not a feature is highly correlated with the class or not highly correlated with any of the other features. On the other hand, the fscmrmr algorithm finds an optimal set of features that are mutually as dissimilar as possible and can effectively represent the response variable. The algorithm minimizes a feature set's inconsistency and maximizes the relevance of a feature set to the answer variable. MATLAB built-in functions can be used for CFS,

*Baseline correction of PPG waveform. (a) PPG waveform with the baseline wandering and fourth degree*

After the features were extracted, the feature matrix was trained with machine learning algorithms. The Regression Learner App of MATLAB 2019b was used to estimate the BP. Five different algorithms [linear regression, regression trees, support vector regression (SVR), Gaussian process regression (GPR), and ensemble trees] with their variations to a total of 19 algorithms were trained using the 10-fold

ReliefF, and fscmrmr feature selection algorithms.

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**Figure 10.**

**Figure 11.**

*Overview of feature extraction.*

*Evaluation of the outperforming algorithms for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) after optimization.*

cross-validation. Out of all these algorithms, two best performing algorithms, Gaussian process regression and ensemble trees, were tested.

#### **3.6 Results**

To evaluate the performance of the ML algorithms for estimating BP, it is important to select proper metrics, i.e., four in this case and are stated below. Here, Xp is the predicted data while the ground truth data is X and n is the number of samples.

$$\text{Mean absolute error, MAE} = \frac{1}{n} \sum\_{n} \left| X\_p - X \right| \tag{1}$$

$$\text{Mean squared error}, \text{MSE} = \frac{\sum \left| X\_p - X \right|^2}{n} \tag{2}$$

$$\text{Root mean squared error, RMSE} = \sqrt{\frac{\sum \left| X\_p - X \right|^2}{n}} = \sqrt{\text{MSE}} \tag{3}$$

$$\text{Correlation coefficient}, R = \sqrt{1 - \frac{\text{MSE} \, \text{(Model)}}{\text{MSE} \, \text{(Baseline)}}}\tag{4}$$

$$\text{where } \text{MSE} \, \text{(baseline)} = \frac{\sum \left| X - \text{mean} \, (X) \right|^2}{n}$$

When using the Regression Learner App in MATLAB, the above criteria are automatically calculated by MATLAB and these values can be used to evaluate the performance of the algorithms.

In **Table 1**, it can be noticed that the ReliefF feature selection algorithm produced the best result when combined with GPR. The feature selected using a combination of ReliefF and GPR performed the best in estimating SBP, while CFS and GPR performed best for DBP. Moreover, R scored 0.74 and 0.68 for SBP and DBP, respectively, which means that there is a strong correlation with the predictors and the ground truth.

### **4. Wearable real-time heart attack detection**

Myocardial infarction (MI), commonly known as heart attack, is considered one among the reasons of human death and disability worldwide. Although a heart attack is life threatening, it has early symptoms and signs that could be measured to help save many lives and to avoid consequences. There are three types of a heart attack: STEMI, NSTEMI, and coronary spasm, refer **Figure 12**.

Several ECG ambulatory monitoring systems are available, but they require time and effort to acquire ECG signals from patients. Furthermore, the ECG data have to be sent to professionals for diagnostic analysis. A wearable ECG device can be a solution to acquire real-time ECG signals from the patient. Then, signals are interpreted using built-in algorithms to observe abnormality events of ECG signals and sudden heart attack. Therefore, proper action can be made.

In terms of algorithm, some systems used linear classification and support vector machine (SVM). Some research studies have used the PhysioBank MI database to develop SVM-based real-time MI detection system with a classification accuracy of 90%. Another work came up with the solution for the motion artifact correction for the two-electrode ECG system with an optimized adaptive filter.

In this section, a wearable heart attack detector and alarming system is discussed, which can be used to reduce road accidents that might result from the driver being precipitated by a heart attack, details of which are present in [11]. A portable wearable system that can continuously monitor the heart condition for any early symptoms could inform the patient to pull over the vehicle safely before losing consciousness to avoid potentially fatal consequences. Moreover, medical caregivers could arrive and provide the necessary medical procedures that rescue the consequences of a heart attack.

#### **4.1 Wearable system prototype description**

The real-time heart attack monitoring wearable prototype system consists of two subsystems as shown in **Figure 13**. The subsystems communicate wirelessly using Bluetooth low energy (BLE) technology.

A wearable sensor subsystem is responsible for real-time ECG signal acquisition, amplification, filtering, digitization, and wireless transmission. The device is attached to a chest belt to be worn by a driver as shown in **Figure 13**. The device

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system using BLE.

**Figure 12.**

**Figure 13.**

*NSTEMI (c).*

RPi3 using a low power shield.

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

includes three dry electrodes (one electrode for reference and two electrodes for differential acquisition) connected to an analog front end (AFE) and an RFduino microcontroller with an embedded BLE module. Furthermore, the designed module incorporates several features to enhance the overall performance including lead-off detection, single-supply operation, adjustable gain control, rail-to-rail output, a three-pole adjustable low-pass filter (LPF), a two-pole adjustable high-pass filter (HPF), and an integrated right-leg drive (RLD). A notch filter in Wien bridge configuration was used to remove the line frequency (50 Hz) from the measured ECG signal. The acquired and preprocessed ECG signal was sent to the decision-making

*Block diagram of the wearable heart attack detection prototype.*

*Comparison of the ST segment variations in a normal subject (a) and in MI patients with STEMI (b) and*

The intelligent heart attack detection and warning subsystems are trained using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) ST Change Database [12]. This subsystem consists of a single board computer, Raspberry Pi 3 (RPi3). The acquired ECG signal from RFduino is delivered to RPi3 over the Bluetooth low energy (BLE) interface. ECG data are buffered for 10 s, and the baseline drift is corrected, segmented to ECG beats (one ECG trace), and smoothened using a digital filter. Furthermore, the subsystem incorporates SIM 908 global system for mobile communications (GSM) and the global positioning system (GPS) module; the module is directly connected to

Twenty-two different machine learning (ML) algorithms were trained for realtime ECG classification. Experiments were done in two stages. In the first stage, the public labeled database "MIT-BIH ST Change Database" covers both normal and abnormal subjects. Twenty-eight subjects' ECG recordings with 14 subjects' ECG recordings were normal, seven patients had T-inversion, and the other patients

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

**Figure 12.**

*Sports Science and Human Health - Different Approaches*

where ( ) ( )

**4. Wearable real-time heart attack detection**

attack: STEMI, NSTEMI, and coronary spasm, refer **Figure 12**.

and sudden heart attack. Therefore, proper action can be made.

the two-electrode ECG system with an optimized adaptive filter.

∑ − <sup>=</sup>

MSE baseline

performance of the algorithms.

ground truth.

MSE Model Correlation coefficient, 1 MSE Baseline

When using the Regression Learner App in MATLAB, the above criteria are automatically calculated by MATLAB and these values can be used to evaluate the

the best result when combined with GPR. The feature selected using a combination of ReliefF and GPR performed the best in estimating SBP, while CFS and GPR performed best for DBP. Moreover, R scored 0.74 and 0.68 for SBP and DBP, respectively, which means that there is a strong correlation with the predictors and the

In **Table 1**, it can be noticed that the ReliefF feature selection algorithm produced

Myocardial infarction (MI), commonly known as heart attack, is considered one among the reasons of human death and disability worldwide. Although a heart attack is life threatening, it has early symptoms and signs that could be measured to help save many lives and to avoid consequences. There are three types of a heart

Several ECG ambulatory monitoring systems are available, but they require time and effort to acquire ECG signals from patients. Furthermore, the ECG data have to be sent to professionals for diagnostic analysis. A wearable ECG device can be a solution to acquire real-time ECG signals from the patient. Then, signals are interpreted using built-in algorithms to observe abnormality events of ECG signals

In terms of algorithm, some systems used linear classification and support vector machine (SVM). Some research studies have used the PhysioBank MI database to develop SVM-based real-time MI detection system with a classification accuracy of 90%. Another work came up with the solution for the motion artifact correction for

In this section, a wearable heart attack detector and alarming system is discussed, which can be used to reduce road accidents that might result from the driver being precipitated by a heart attack, details of which are present in [11]. A portable wearable system that can continuously monitor the heart condition for any early symptoms could inform the patient to pull over the vehicle safely before losing consciousness to avoid potentially fatal consequences. Moreover, medical caregivers could arrive and provide the necessary medical procedures that rescue the conse-

The real-time heart attack monitoring wearable prototype system consists of two subsystems as shown in **Figure 13**. The subsystems communicate wirelessly

A wearable sensor subsystem is responsible for real-time ECG signal acquisition, amplification, filtering, digitization, and wireless transmission. The device is attached to a chest belt to be worn by a driver as shown in **Figure 13**. The device

mean

*X X n*

2

( ) ( )

*R* = − (4)

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quences of a heart attack.

**4.1 Wearable system prototype description**

using Bluetooth low energy (BLE) technology.

*Comparison of the ST segment variations in a normal subject (a) and in MI patients with STEMI (b) and NSTEMI (c).*

**Figure 13.** *Block diagram of the wearable heart attack detection prototype.*

includes three dry electrodes (one electrode for reference and two electrodes for differential acquisition) connected to an analog front end (AFE) and an RFduino microcontroller with an embedded BLE module. Furthermore, the designed module incorporates several features to enhance the overall performance including lead-off detection, single-supply operation, adjustable gain control, rail-to-rail output, a three-pole adjustable low-pass filter (LPF), a two-pole adjustable high-pass filter (HPF), and an integrated right-leg drive (RLD). A notch filter in Wien bridge configuration was used to remove the line frequency (50 Hz) from the measured ECG signal. The acquired and preprocessed ECG signal was sent to the decision-making system using BLE.

The intelligent heart attack detection and warning subsystems are trained using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) ST Change Database [12]. This subsystem consists of a single board computer, Raspberry Pi 3 (RPi3). The acquired ECG signal from RFduino is delivered to RPi3 over the Bluetooth low energy (BLE) interface. ECG data are buffered for 10 s, and the baseline drift is corrected, segmented to ECG beats (one ECG trace), and smoothened using a digital filter. Furthermore, the subsystem incorporates SIM 908 global system for mobile communications (GSM) and the global positioning system (GPS) module; the module is directly connected to RPi3 using a low power shield.

Twenty-two different machine learning (ML) algorithms were trained for realtime ECG classification. Experiments were done in two stages. In the first stage, the public labeled database "MIT-BIH ST Change Database" covers both normal and abnormal subjects. Twenty-eight subjects' ECG recordings with 14 subjects' ECG recordings were normal, seven patients had T-inversion, and the other patients

experienced long-term recordings that showed ST elevation and depression. The system was tested on normal subjects, and an ECG simulator was used to simulate abnormal ST-elevated MI situations to test the performance of the complete system in real time.

#### **4.2 Preprocessing**

Due to body movements, even respiration, muscle signal, and power line interference of 50 Hz, filtering stage was essential to filter the signal and eliminate the inherent noises. In addition, ECG was often affected by artifacts constituted through electrodes or the interference of the signal processing hardware. Finite impulse response filter (FIR) based on window method was used to smooth the noisy signal by slicing the array of data into selected length windows, computing averages of the data within that range, and maintaining the process throughout the dataset using the moving-average filter.

Baseline wander correction technique and continuous wavelet transformation were used for synthesizing the ECG signal; the wavelet was able to show how the frequency component varied within certain ranges of time. The ECG signals were segmented into traces. A total of 3500 ECG traces over 28 different subjects were considered. Therefore, the total number of ECG traces considered in this study was 10,500.

#### **4.3 Feature extraction**

Different features were investigated from time, frequency, and time-frequency domains. **Table 2** shows the t-domain, the f-domain, and the (t, f) domain features extracted.

### **4.4 Training process**

A five-fold cross-validation technique was used in the testing and training process. Weighted performance matrix was created by averaging the results of all the iterations. The averages of all scoring matrices were calculated for the four iterations along with their standard deviation. Performance evaluations of three different timefrequency distributions (TFDs) were calculated to identify which TFD produced higher accuracy. In addition, the receiver operating characteristic (ROC) analysis was used to evaluate the performance of the ML algorithm for classification confusion matrix and several standard statistical evaluation parameters were used to evaluate the performance of the algorithms including sensitivity, false positive rate (FPR), F1 score, and accuracy.

**85**

**Table 4.**

*Evaluation parameters in classifying ST elevation.*

*Machine Learning in Wearable Biomedical Systems DOI: http://dx.doi.org/10.5772/intechopen.93228*

for the extended modified B distribution.

All the features listed in **Table 2** were extracted to evaluate the performance of each feature. AUC were calculated as shown in **Table 3**. All features that scored a minimum of 0.5 and above were useful to apply to the classifiers. It was noticed that all the selected features fulfilled the requirement for all of the distributions. Hence, all the features were used for training, validation, and testing. A five-fold cross-validation was used to compute the ML algorithms validation accuracy. **Tables 4** and **5** demonstrate the accuracies resulting from classifying ST elevation and T-wave inversion for the three different (t, f) distributions. Also, both tables showed the two best performing algorithms, the support vector machine (SVM) and the k-nearest neighbors (KNNs). It was noticed that SVM outperformed KNN

As shown from **Tables 4** and **5**, EMBD outperformed the others in classifying ST elevation and T-wave inversion. Moreover, the variance showed that the variation for different iterations was at the minimum for the EMBD distribution (**Table 3**). Thus, EMBD distribution was more robust to noisy data. EMBD distributions were implemented for real-time classification using Python. The results showed that both the recall and the precision were reasonable for reliable detection, and for both positive and negative classifications. This can be observed in the F score as well.

**Features WVD SPEC EMBD**

Mean 0.53 0.67 0.53 0.75 0.53 0.75 Variance 0.51 0.71 0.51 0.66 0.51 0.58 Skewness 0.68 0.57 0.68 0.67 0.68 0.57 Kurtosis 0.75 0.57 0.75 0.69 0.75 0.56

Spectral flux 0.76 0.82 0.76 0.83 0.76 0.55

*Results of the ROC analysis of the t-domain, f-domain, and (t, f) domain features for the ST elevation*

**Parameters/ML algorithms WVD SPEC EMBD**

Recall (TPR) (%) 92 89 89 90 99.1 96.7 FPR (%) 11 12 14 13 1.7 3.8 Precision (%) 89 88 86 87 98.3 96.2 F score (%) 90.5 88.9 87.5 86 98.7 97 Accuracy (%) 87.1 86.4 85.3 84.2 97.4 95.9

**Original Joint (t, f) Original Joint (t, f) Original Joint (t, f)**

0.50 0.74 0.50 0.65 0.50 0.58

0.54 0.71 0.54 0.79 0.54 0.68

0.81 0.71 0.81 0.65 0.81 0.59

**SVM KNN SVM KNN SVM KNN**

**4.5 Results**

Coefficient of variation

Spectral flatness

Spectral entropy

**Table 3.**

*detection.*

#### **Table 2.**

*Features extracted from the ECG traces.*


**6.2.1 Propagating structures**

Propagating systems for real-time studies are generally closed structures modied with holes for sample observation and perfusion, and for online monitoring of biochemical or biophysical parameters.

They are mostly TEM cells and rectangular waveguides as described in (Linz et al., 1999), where the electrophysiological activity of myocyte cells was recorded, through the patchclamp technique, during the exposure to 900 MHz and 1800 MHz CW. The systems were equipped with two holes: one in the top wall to insert the recording electrode and the other in the bottom to observe the sample with a microscope. The solution adopted to avoid interference between the electric eld inside the guide and the metallic wire of the patchclamp electrode was the elongation of the glass microelectrodes used for sealing the cell in order to place the wire outside the exposure device. The power efciency was not bad: 1.66 (W/kg)/W at 900 MHz and 3.16 (W/kg)/W at 1800 MHz.

A rectangular waveguide (Lambrecht et al., 2006), operating between 0.75–1.12 GHz, was also used to measure, through a force transducer, the effects of the RF field on skeletal muscle inserted in a bath placed in the center of the waveguide. The waveguide was modified with slots in the walls to allow connection with measurement, control, and stimulating devices outside the system. The power efciency was good (> 3 (W/kg)/W) and SAR homogeneity, around 79 %, was in line with minimal requirements for *in vitro* experiments (homogeneity > 70 %). A modied rectangular waveguide was used for delivering CW or Universal Mobile Telecommunications System (UMTS) signals to neuronal networks during electrophysiological recordings from multiple electrodes (Koester et al.,

Real Time Radio Frequency Exposure for Bio-Physical Data Acquisition 307

A modied stripline, where the sample holder (a cuvette) and the temperature regulating chamber were part of the dielectric substrate, was described in (Ramundo-Orlando et al., 2004). The system was placed inside a spectrophotometer for monitoring the enzymatic activity of ascorbate oxidase trapped in liposomes during the exposure to a CW at 2.45 GHz.

Only one example of resonant structure used for real-time acquisition is presented in literature. The resonant system proposed by Hagan *et al.* (Hagan et al., 2004) was a short circuited rectangular waveguide designed to expose neural cells in the frequency range of 0.75–1.12 GHz while monitoring catecholamine release. The guide had slots on the plates to allow the communication between the cell perfusion apparatus, placed inside the

Even for radiating structures only one system has been published. A radiating system, based on a horn antenna, with the perfusion chamber placed in the far-eld region, was used in (Yoon et al., 2006). The biological protocol was the same described in (Hagan et al., 2004), but the exposure frequencies were higher (1-6 GHz). For this higher frequency range, a standard waveguide is too small to accommodate the sample and the cellperfusion apparatus and a radiating system is necessary. However, this solution required a special arrangement to avoid interference with the experimental equipment that was shielded in a conducting box behind the perfusion chamber and a layer of absorber material was used to prevent electric eld reection. The whole system was placed within

Real-time data acquisition in biomedicine is employed in a lot of different applications, both *in vitro* and *in vivo*. Examples are given in monitoring of chemical reactions (Shutes and Der, 2005), gene expression (Gubern et al., 2009), and drug release (Chouhan and Bajpai, 2009). Concerning living animals, the real-time acquisition is usually based on non-invasive

The common issue in both real-time *in vitro* and *in vivo* investigations for biomedicine is that the experimental acquisition of physiological data is performed simultaneously with the

Focus of this chapter was a review of the exposure systems used in real-time biological experiments, i.e. in those investigations where bio-chemical or bio-physical data are acquired from the sample simultaneously with the exposure to the EM field.  This is also the case of bioelectromagnetic investigations.

system and RF EM fields through *in vitro* electrophysiological techniques.

when applied to real-time data acquisition is still lacking.

**6.2.2 Resonant structures**

waveguide, and the exterior.

**6.2.3 Radiating structures**

an anechoic chamber.

**7. Conclusion**

2007). The neuronal network was cultivated on a microsensor chip tted into a recess in the guide to avoid short circuiting the measuring probes while exposing the neuronal cells.

For a similar experiment at 1800 MHz, a different solution was proposed in (Merla et al., 2011). The exposure system consisted of a TEM cell open in correspondence of the lateral walls. The multiple electrode array used for the electrophysiological recordings was inserted in a circular hole in the TEM cell bottom plate. The power efficiency of 3.2 (W/kg)/W was comparable to that of the closed systems described above.

A waveguide terminated with an exposure cell containing the sample was used to expose heart slices (Pakhomov et al., 2000) and brain slices (Pakhomov et al., 2003) to high-power microwave pulses (repetition frequency 9.2 GHz). The system had an extremely high efciency of 3.3 (kW/kg)/W which decreased about twofold per millimeter with distance from the waveguide aperture. In this case, the sample was used as the load of the guide; the impedance matching was achieved through a sapphire matching plate to maximize the power absorbed by the slices.

To record brain slices electrophysiological activity simultaneously with the exposure to a 700 MHz eld, Tattersall *et al.* (Tattersall et al., 2001) utilized a waveguide made of two parallel plates. In this case, as in (Linz et al., 1999), the top and bottom plates of the guide had holes to observe the sample and insert both stimulating and recording electrodes. The coupling between the electric field and the electrodes was not avoided since the electrodes were placed at an angle of about 45° to the electric eld (Misfud et al., 2007). The estimated efciency value was lower than 0.03 (W/kg)/W.

A completely different solution, based on an open coplanar waveguide was adopted in studies involving patch-clamp recordings from neuronal cells (Liberti et al., 2004) and eld potential recordings from brain slices (Paffi et al., 2007). Both systems operate in the 800– 2000-MHz frequency band and differ from each other by the distance between the central and lateral conductors because of the different size of biological samples to be exposed to the EM fields. The open planar geometry and the transparent glass substrate allowed easy access to the samples through the microscope and the electrodes; the electric and magnetic elds were conned in a small volume around the surface that guaranteed the avoidance of interference with the data acquisition setup and high efciency values (> 17 (W/kg)/W) at all frequencies.

To perform patch-clamp recordings from the ear hair cells of Corti organ, three exposure systems were described in (El Ouardi et al., 2011), operating at 900 MHz, 1.8 GHz and 2 GHz, respectively. All of them are based on the concept of the fin-line: a quasi-planar transmission line structure embedded in a metallic rectangular waveguide. In this case, the two fins were placed in the magnetic field plane of the waveguide. The chamber containing the samples was placed onto the two ns with a slot in between, where the exposure eld concentrates, and inserted inside the guide through a circular opening in the top plate. This opening was also used to insert the electrodes and the microscope objective during the experiments. The efficiency was very high (> 40 (W/kg)/W for all systems), with a good homogeneity of 90 %. The drawback was the narrow band that imposed the fabrication of three different systems, one for each frequency band of interest (El Ouardi et al., 2011).

A modied stripline, where the sample holder (a cuvette) and the temperature regulating chamber were part of the dielectric substrate, was described in (Ramundo-Orlando et al., 2004). The system was placed inside a spectrophotometer for monitoring the enzymatic activity of ascorbate oxidase trapped in liposomes during the exposure to a CW at 2.45 GHz.


**A Comparative Study of Analytical Methods for Determination of Polyphenols in Wine by HPLC/UV-Vis, Spectrophotometry and Chemiluminometry**

Vesna Weingerl\*

*University of Maribor, Faculty of Agriculture and Life Sciences, Hoče, Slovenia*


**Mitochondrial Dysfunction on the Toxic Effects of Anticancer Agents— From Lab Bench to Bedside**

Mariana Ponte Cardoso Ribeiro, Armanda Emanuela Castro e Santos and José Barata Antunes Custódio

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/59488


*Editorial* **Organic Conductors**

**Toshio Naito 1,2,3**

Since the pioneering work concerning organic semiconductors in the middle of the 1900s, organic conductor research has experienced a series of milestones, from metallic to superconducting charge-transfer complexes (1980s–1990s). The history of these materials is reviewed in this Special Issue by considering a body of references [1].

The publication "Organic Conductors" covers various solids containing both organic and inorganic species with a variety of shapes and dimensions. The physical properties of these materials include electrical, magnetic, structural, optical, dielectric, and mechanical. The most prominent feature of organic conductors and related materials is the wide variety of degrees of freedom, which enables peculiar electronic states, physical properties, and phase transitions that are otherwise unobserved. As a result, even an insulating organic crystalline material can be a center of interest in this field if it provides an important piece of information concerning the mechanism of superconductivity, for example, or sheds light on other subjects of broad interest from the abovementioned point of view.

In 2022, researchers in this ever-expanding and developing field are looking for further interesting and exciting targets that are yet to be explored. On such a memorial occasion, this Special Issue, entitled "Organic Conductors", has collated 25 papers (plus editorials) from 11 countries. Readers will enjoy the latest developments in new materials, ideas, and methodologies, which will propel the field in a new direction and age.

The Special Issue consists of the following papers and reviews:

	- Kubo et al. reported a series of nickel–dithiolene complexes fused with bulky cycloalkane substituents to study their steric-based effects on molecular arrangements [2].
	- Kadoya et al. reported the structural and physical properties of a new organic Mott insulator with a *θ*-type molecular arrangement [3].
	- Mroweh et al. reported new chiral conducting salts based on ethylenedithiotetrathiafulvalene (EDT-TTF) derivatives, presenting their crystal structures, extended Hückel band structures, and electrical properties [4].
	- Sakaguchi et al. reported a new single-component molecular conductor, [Au(etdt)2]·THF (etdt = ethylenedithiotetrathiafulvalenedithiolate), that is, the synthesis, structure, and physical properties of a neutral gold-dithiolenecomplex [5].
	- Akutsu et al. reported three types of new organic conductors containing HOC2H4SO3 − anions and discussed their electrical properties in terms of the Madelung energies [6].
	- Sato et al. reported new stable neutral radical species, MIII(Pc)Cl2·THF (M = Co or Fe, Pc = phthalocyanine, THF = tetrahydrofuran), as three-dimensional, singlecomponent molecular conductors [7].
	- Koyama et al. reported the synthesis and structural, spectroscopic, and electrical properties of a new molecular conductor with a weak hydrogen-bonding network [8].

**Citation:** Naito, T. Organic Conductors. *Crystals* **2022**, *12*, 523. https://doi.org/10.3390/ cryst12040523

Received: 14 March 2022 Accepted: 8 April 2022 Published: 9 April 2022

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**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

	- Rohwer et al. reported the effects of deuteration on the transport properties of quasi-one-dimensional Fabre salts, (TMTTF)2*X* (TMTTF = tetramethyltetrathiafulvalene, *X* = Br, PF6, and ClO4), and discovered various conduction properties such as Mott insulators, variable range hopping, and activated band transport with a temperature-dependent bandgap [9].
	- Kitou et al. reported an experimental method for use in estimating valence electron densities, that is, frontier orbitals, in the solid state based on X-ray diffraction data [10].
	- Yoshino et al. reported the crystal structures and electrical resistivities of a series of TMTSF (TMTSF = tetramethyltetraselenafulvalene) salts with unusual stoichiometries and determined the valence state of TMTSF in each type of salt using quantum chemistry calculations [11].
	- Yamamoto et al. reported a new system for measuring the pyroelectricity of small ferroelectric single crystals [12].
	- Hino et al. reported the current- and voltage-dependence of the heat capacity of a single crystal of a charge glass compound, θ-(BEDT-TTF)2CsZn(SCN)4 (BEDT-TTF = bis(ethylenedithio)-tetrathiafulvalene) [13].
	- Hiraki et al. reported 77Se-NMR studies on λ-type BETS superconductors, λ-(BETS)2Fe1−xGaxCl4 (BETS = bis(ethyleneditho)tetraselenafulvalene), to examine the π-spin polarization affected by the localized 3*d* spins on Fe atoms [14].
	- Tsumuraya et al. reported first-principle density functional theory calculations of the charge-ordered phase of α-(BEDT-TTF)2I3, which is closely related to Dirac electron systems [15].
	- Roy et al. reported accurate zero-temperature density matrix renormalization group calculations for κ-(BEDT-TTF)2X, the most studied family of organic superconductors, and concluded that magnetic fluctuations in the effective half-filled band approach do not drive superconductivity in these and related materials [16].
	- Ménard et al. reported a one-dimensional alternating extended Hubbard model at quarter-filling based on a renormalization group method to examine structural instabilities in Fabre and Bechgaard salts and related organic conductors [17].
	- Suzumura et al. calculated electrical transport in nodal line semimetals of singlecomponent molecular conductors to examine the effects of acoustic phonon scattering on electrical conductivity [18].
	- Naito et al. reported a method of calculating intermolecular interactions in disordered molecular charge-transfer complexes of STF (STF = bis(ethylenedithio) diselenadithiafulvalene) by proposing a new interpretation or usage of wavefunctions [19].
	- Kesharpu et al. calculated the evolution of the shape and volume fraction of superconducting domains in relation to temperature and anion disorder in a highly anisotropic organic superconductor, (TMTSF)2ClO4 [20].
	- Cui et al. reported the high-pressure crystal structure and magnetoresistance of a single-component molecular conductor [Pt(dddt)2] (dddt = 5,6-dihydro-1,4 dithiin-2,3-dithiolate), a theoretically expected three-dimensional Dirac electron system, under high pressure [21].
	- Yasuzuka reviewed the interplay between the vortex dynamics and superconducting gap structures in layered organic superconductors containing strongly correlated electron systems. Particular attention was paid to the symmetry of

the superconducting gaps, which is a key feature in understanding the pairing mechanism [22].

	- Manabe et al. reported reversible structural and magnetic transformations in inorganic ladder compounds; these transformations are dependent on the gasphase chemical species absorbed in single crystals [23].
	- Kawasugi et al. reported doping effects using field-effect transistors consisting of electric double layers of charge-ordered insulators, α-(BEDT-TTF)2I3, and α-(BETS)2I3 [24].
	- Gou et al. reported experimental and modelling studies of a series of highperformance inorganic semiconductor devices based on doped GaAs and related materials [25].

The Guest Editor would like to express his sincere thanks to all of the authors for their contributions to this Special Issue, as well as to all of the reviewers for the time and effort expended to provide valuable feedback to the authors. Special thanks also go to the *Crystals* editorial office members, Ms. Debbie Yang in particular, for their continuous collaboration, timely communication, and efficient support, along with their friendly and professional attitude; without their contributions, the publication of this Special Issue would not have been possible. The Guest Editor hopes that this Special Issue will encourage more and more scientists to join this field to further expand the horizon and to discover new possibilities of molecular conductors.

**Funding:** The author is grateful for the financial support from a Grant-in-Aid for Challenging Exploratory Research (18K19061) of JSPS, the Iketani Science and Technology Foundation (ISTF; 0331005-A), the Research Grant Program of the Futaba Foundation, the Casio Science Promotion Foundation, and an Ehime University Grant for Project for the Promotion of Industry/University Cooperation.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


**1. Introduction**

In wandering river channels under natural conditions, the incoming water, sediment conditions, and boundary conditions are complex and changeable. The riverbed is wide and straight. The sandbars are dotted in the river channel, the branches are crisscrossed vertically and horizontally, and their shapes are different. The positions of the beaches and water often change. Taking the Yellow River as an example, the Baihe Town Gaocun reaches the lower reaches of the Yellow River, which is a typical wandering river. The length of the reach is 299 km, the distance between the embankments on both banks is generally ~10 km, the maximum width is 20 km, and the longitudinal gradient of the river is 1.72–2.65‱. The riverbed section is wide and shallow and the channel width is 1.5–3.5 km. Under natural conditions, the river channel is densely covered with sandbars, scattered water flow, and numerous branching streams, sometimes up to 4–5 strands [1–3]. The river regime changes frequently and the river facies coefficient under flat beach flow is 20–40. Since the 1980s, the wandering section of the lower Yellow River has been regulated. During the

**Citation:** Xu, L.; Jiang, E.; Zhao, L.; Li, J.; Zhao, W.; Zhang, M. Research on the Asymmetry of Cross-Sectional Shape and Water and Sediment Distribution in Wandering Channel. *Water* **2022**, *14*, 1214. https:// doi.org/10.3390/w14081214

Academic Editors: Qiting Zuo, Xiangyi Ding, Guotao Cui and Wei Zhang

Received: 28 February 2022 Accepted: 7 April 2022 Published: 9 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

"National Projects of the Eighth Five-Year Plan" period, the Yellow River regulation workers analyzed the laws of the evolution of the wandering river channel in the lower Yellow River and put forward the "slightly curved regulation scheme". After the implementation of the scheme, the swing range of the main stream and boundary of the wandering river channel was significantly reduced and the river regime of most river sections was initially controlled, which protected the embankment's safety and relieved the downstream flood control pressure to a certain extent. Since 2006 especially, the process of river regulation has been accelerated, the density of regulation works has increased significantly, and the "wide, shallow, scattered, and chaotic" channel shape has been significantly improved [4–6]. At present, there are still some challenges in the wandering river regime which require thorough investigation and excellent river regulation engineering measures [7,8]. River regulation works mainly include control, guidance, and dangerous works [9]. Under the action of regulation works, the wandering range of the river regime has been effectively controlled [10] from 5.5 km in 2000 to 3 km in 2006. In recent years, the swinging range of the main slide of some rivers has become smaller, mostly within 0.5 km.

At the same time, a long-term series of studies on the stabilization of the wandering river regime reported many relevant results that provide constructive opinions on improving the stability of the wandering river regime. Yaoxian et al. [11] found that the longitudinal velocity distribution in a bend is related to the incoming water conditions and boundary conditions through a large-scale bend model test and observation. Liu Yan [12] adopted a slightly curved river regulation scheme to study the impact of changes in the regulation engineering conditions on the river regime of the wandering river in the lower reaches of the Yellow River. The author [8] also studied the change in water and sediment conditions after the construction of Xiaolangdi reservoir. The mismatch between the river regulation project and the new water-sediment relationship led to adjustment of the downstream river regime and frequent occurrence of the abnormal river regime. Hongwu et al. [13] studied the influence of the length of the regulation project on the regulation effect of the wandering river section using the natural river model test, analyzed the adaptability of the built regulation project to medium and small water [14], and discussed the variation law of river length in the straight river section [15,16]. Junhua et al. [7] analyzed the role of downstream wandering river regulation in the new period, analyzed the existing problems of wandering river, and proposed several control countermeasures such as improving river regulation engineering measures. Xinjie [17] studied the impact of beach area project on erosion and deposition in the lower Yellow River. The lower Yellow River has a slightly curved river regulation. River regulation works have strong constraints on the river channel; the riverbed deformation is mainly undercut, the channel width depth ratio is reduced, the river cross-section tends to be narrow and deep, and the river regime tends to be stable [18–20]. In the free-developing river bend, the river boundary constraint is weak, the riverbed deformation is mainly widening, the river width depth ratio increases, the river cross-section tends to be broad and shallow, and the river regime changes significantly. The sediment transport capacity of the broad and shallow areas is weaker than that of the narrow and deep areas, which easily aggravates river sedimentation [21,22] and enhances the heterogeneity of riverbed composition, resulting in a continuous change of section shape with water flow and poor stability of the river regime. Following construction of the river regulation project, the evolution of the river regime is limited to a certain extent, the swing range of the central slip of the wandering river channel is significantly reduced, and the river facies relationship changes accordingly [23,24].

The study discussed how the construction of the river regulation project improved and controlled the river regime, resulting in a new river bed shape, especially in the river section where the river regulation works are relatively perfect and the river section is relatively close to the mainstream. The impact on the river cross-section after the construction project and the change in water and sediment transport and riverbed deformation caused by the adjustment of river cross-section were also investigated. The riverbed evolution differs from the natural river channel. The bend circulation in natural rivers causes a prominent

asymmetry in the distribution of river sections and water and sediment factors. At present, research on changing the shape of river sections after construction of the river regulation project mostly remains at the level of qualitative description, and quantitative calculation is rarely involved. This paper proposed an asymmetry index and calculation method to characterize the river section shape and water and sediment factors. Using the formula for the river section and water and sediment factors along with the transverse distribution, this study quantitatively calculated the asymmetry of section shape and water and sediment factors along with the transverse distribution of the typical reach of the wandering section of the lower Yellow River before and after the construction project. This revealed the reasons for the improvement in river sediment transport capacity after the construction project. lation method to characterize the river section shape and water and sediment factors. Using the formula for the river section and water and sediment factors along with the transverse distribution, this study quantitatively calculated the asymmetry of section shape and water and sediment factors along with the transverse distribution of the typical reach of the wandering section of the lower Yellow River before and after the construction project. This revealed the reasons for the improvement in river sediment transport capacity after the construction project. **2. Materials and Methods**

caused by the adjustment of river cross-section were also investigated. The riverbed evolution differs from the natural river channel. The bend circulation in natural rivers causes a prominent asymmetry in the distribution of river sections and water and sediment factors. At present, research on changing the shape of river sections after construction of the river regulation project mostly remains at the level of qualitative description, and quantitative calculation is rarely involved. This paper proposed an asymmetry index and calcu-

#### **2. Materials and Methods** *2.1. Construction of River Regulation Project in the Lower Yellow River*

#### *2.1. Construction of River Regulation Project in the Lower Yellow River* With the enhancement of river development and utilization in the last decade, ac-

*Water* **2022**, *14*, x FOR PEER REVIEW 3 of 18

With the enhancement of river development and utilization in the last decade, according to the laws of evolution of a wandering natural river channel, some river regulation projects have been built alternately on the concave bank of the river channel which can transport flood and sediment during large floods and maintain the stability of the river regime in small and medium floods (Figure 1). cording to the laws of evolution of a wandering natural river channel, some river regulation projects have been built alternately on the concave bank of the river channel which can transport flood and sediment during large floods and maintain the stability of the river regime in small and medium floods (Figure 1).

(**a**) (**b**)

**Figure 1.** River channel with a finite control boundary: (**a**) Local reach in the lower Yellow River; (**b**) river regulation project in the lower Yellow River. **Figure 1.** River channel with a finite control boundary: (**a**) Local reach in the lower Yellow River; (**b**) river regulation project in the lower Yellow River.

In order to reflect the construction of wandering river channel project, the density of a river regulation project was defined as the ratio of the total length of the river regime control project in the wandering river channel to the length of river channel. This index indirectly reflects the restraint ability of river regulation project to the river regime. The construction of the Baihe–Gaocun river regulation project of the wandering river in the lower Yellow River was collected and river regulation project density from 1960 to 2014 was drawn, as shown in Figure 2. The river regulation project here only included the control project that restricted the river regime, excluding dangerous projects. As can be seen from Figure 2, as of 2014, the density of the wandering river regime control project has In order to reflect the construction of wandering river channel project, the density of a river regulation project was defined as the ratio of the total length of the river regime control project in the wandering river channel to the length of river channel. This index indirectly reflects the restraint ability of river regulation project to the river regime. The construction of the Baihe–Gaocun river regulation project of the wandering river in the lower Yellow River was collected and river regulation project density from 1960 to 2014 was drawn, as shown in Figure 2. The river regulation project here only included the control project that restricted the river regime, excluding dangerous projects. As can be seen from Figure 2, as of 2014, the density of the wandering river regime control project has reached more than 70%.

#### reached more than 70%. *2.2. Quantitative Characteristics of the Asymmetric Index*

In natural rivers, water flow and riverbeds are intrinsically related. Water flow shapes the riverbed, which in turn restricts water flow. Channel evolution occurs and constantly changes its interactions, e.g., vertical evolution and horizontal evolution. The study of lateral evolution involves bend circulation and other problems which are complex. The asymmetry of the river cross-section is caused by bend circulation which affects the transverse distribution of water and sediment factors, resulting in concave scouring and convex siltation under certain water and sediment conditions. All erosion and deposition evolution of the river is related to asymmetry; therefore, study of the river asymmetry is significant.

For the wandering channel, the asymmetry of the section shape and the distribution of water and sediment factors on the section is obvious and there is a strong causal relationship

between them. River regulation works in the wandering reach of the lower Yellow River are arranged according to the asymmetric characteristics of water and sediment movement. The limited engineering boundary is always arranged on one side of the river, and most of the projects on the opposite side are not arranged. Hence, the river boundary condition was termed the limited control boundary condition in this study. Therefore, the limited control boundary is also an asymmetric boundary. After a large number of river regulation projects were built in the lower Yellow River, the distribution of river water and sediment factors in the cross-section has been strongly disturbed, which is bound to change significantly under natural conditions and in turn affects the cross-sectional shape and the adjustment of the river regime. Therefore, it is important to study the asymmetric variation law of the channel cross-sectional shape and transverse distribution of water and sediment factors under limited control boundary conditions. *Water* **2022**, *14*, x FOR PEER REVIEW 4 of 18

**Figure 2.** Density of wandering river regulation works in the lower Yellow River from 1960 to 2014. **Figure 2.** Density of wandering river regulation works in the lower Yellow River from 1960 to 2014.

*2.2. Quantitative Characteristics of the Asymmetric Index*  In natural rivers, water flow and riverbeds are intrinsically related. Water flow shapes the riverbed, which in turn restricts water flow. Channel evolution occurs and constantly changes its interactions, e.g., vertical evolution and horizontal evolution. The study of lateral evolution involves bend circulation and other problems which are com-Here, we discussed some important characteristics of the asymmetry of water and sediment factors along with the transverse distribution, focusing on the comparative analysis of the overall characteristics of the asymmetry of water and sediment factors on the left and right sides of the river. Taking the centerline of the river cross-section under flat discharge as the axis, it was divided into left and right sides, and the square root of the area ratio of each physical quantity on both sides was used to qualitatively describe the asymmetry. Here, regardless of the left and right sides or the left or the right bank bend, the large side index was used as the numerator and the small side as the denominator. Thus, the asymmetry index *AS*i is expressed as

$$AS\_{\rm i} = \sqrt{i\_{\rm max}/i\_{\rm min}} \tag{1}$$

tionship between them. River regulation works in the wandering reach of the lower Yel-

and most of the projects on the opposite side are not arranged. Hence, the river boundary condition was termed the limited control boundary condition in this study. Therefore, the limited control boundary is also an asymmetric boundary. After a large number of river regulation projects were built in the lower Yellow River, the distribution of river water and sediment factors in the cross-section has been strongly disturbed, which is bound to change significantly under natural conditions and in turn affects the cross-sectional shape and the adjustment of the river regime. Therefore, it is important to study the asymmetric variation law of the channel cross-sectional shape and transverse distribution of water

Here, we discussed some important characteristics of the asymmetry of water and

*ASii* i max min = (1)

sediment factors along with the transverse distribution, focusing on the comparative analysis of the overall characteristics of the asymmetry of water and sediment factors on the left and right sides of the river. Taking the centerline of the river cross-section under flat discharge as the axis, it was divided into left and right sides, and the square root of the area ratio of each physical quantity on both sides was used to qualitatively describe the asymmetry. Here, regardless of the left and right sides or the left or the right bank bend, the large side index was used as the numerator and the small side as the denominator.

the transverse distribution of water and sediment factors, resulting in concave scouring and convex siltation under certain water and sediment conditions. All erosion and deposition evolution of the river is related to asymmetry; therefore, study of the river asymwhere *i* is any physical quantity, e.g., river cross-section, cross-sectional velocity, sediment concentration, suspended sediment composition, and sediment-carrying capacity. *AS*i > 1, and the greater the *AS*i , the stronger the asymmetry. When *AS*i = 1, the overall distribution of each physical quantity on both sides of the river section is symmetrical.

metry is significant. For the wandering channel, the asymmetry of the section shape and the distribution of water and sediment factors on the section is obvious and there is a strong causal rela-According to the above definition, we derived a formula for the asymmetry index of the overall distribution of the channel cross-sectional shape as well as water and sediment factors on both sides of the channel cross-section (Equations (2)–(6)), marking the specific contents of each element with the lower corner mark. The asymmetry index of the channel

and sediment factors under limited control boundary conditions.

Thus, the asymmetry index *AS*i is expressed as

cross-sectional shape (*AS*A) as well as those of the overall distributions of velocity (*AS*V), sediment concentration (*AS*S), average suspended sediment size (*AS*dcp), and sediment carrying capacity (*AS*S\*) on both sides of the river cross-section were obtained.

$$AS\_{\rm A} = \sqrt{\frac{A\_{\rm max}}{A\_{\rm min}}} \tag{2}$$

$$AS\_{\rm V} = \sqrt{\frac{V\_{\rm max}}{V\_{\rm min}}} \tag{3}$$

$$AS\_{\rm S} = \sqrt{\frac{S\_{\rm max}}{S\_{\rm min}}} \tag{4}$$

$$AS\_{\rm dcp} = \sqrt{\frac{(d\_{\rm cp})\_{\rm max}}{(d\_{\rm cp})\_{\rm min}}} \tag{5}$$

$$\text{AS}\_{\text{S}\_{\ast}} = \sqrt{\frac{(\text{S}\_{\ast})\_{\text{max}}}{(\text{S}\_{\ast})\_{\text{min}}}} \tag{6}$$

Using the above formula, the asymmetry of the channel cross-sectional shape and the asymmetry of the water and sediment factor distribution caused by the asymmetric adjustment of the channel cross-sectional shape were calculated. The calculation method for the transverse distribution of river water and sediment factors is given below. Using this method, the transverse distribution values of water and sediment factors could be calculated and substituted into Equations (2)–(6) to obtain the quantitative indicators of the asymmetry of the river cross-section and water and sediment factors.