A distinctive peculiarity of this database is that it contains the annotations for each minute of each recording indicating the presence or absence of apnea at the current moment of time . To evaluate the effectiveness of ECG derived parameters for sleep apnea detection , 80 ECG segments of 10 minutes duration , annotated as apnea , and 73 ECG segments of the same duration , annotated as normal sleeping were extracted and investigated . The purpose of this work is to define and compare the informative features for identifying episodes of sleep apnea in ECG by heart rate variability analysis , as well as to choose the classification method that provides the highest accuracy for this task . In this work , we apply machine learning to investigate the effectiveness of an Impulse Radio Ultra-Wide Band ( IR-UWB ) radar panel , in an under-the-mattress configuration , for detecting apnea events in subjects known to have obstructive sleep apnea ( OSA ) . We consider a collection of features , some novel and some inspired by features that worked well for sleep apnea detection using other types of sensors ( i.e. , not IR-UWB ) . To extract the features , we collected a total of 25 hours of data from four subjects as they slept through the night . Abstract We propose a method based on perceptual prosodic features for medium term speaker state classification , particularly sleepiness detection . Unlike existing methods , our features represent spectral characteristics of speech in perceptual bands and also track temporal content omitting any linguistic segmentation . the author took the fifth minute data so that there were 12 data generated , out of the 12 data there were several subject conditions which were drowsy and not . From the results of sleepiness detection , get the data which will then be classified using fuzzy tsukamoto and get drowsy data with results 50 . Each recording was then analyzed offline by a PSG technologist via visual observation of PSG waveforms , who annotated sleep stages N1 and N2 by using the American Academy of Sleep Medicine ( AASM ) scoring rules . Using this as the gold standard , our approach achieved a 87.5 % accuracy for sleep onset detection . The result is better or at least comparable to the other state of the art methods which use either multi-or single- channel based data . Early detection of sleep arousal in polysomnographic ( PSG ) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications , including heart disease and blood pressure fluctuations . In this paper , we present a new automatic detector of non-apnea arousal regions in multichannel PSG recordings . Sleep arousal is a common health problem that negatively affects the quality of sleep . This study investigates the use of a compact convolutional neural network ( CNN ) to classify apnea and non-apnea sleep arousal categories . The goal is to assign a probability of arousal at each sample for each test subject . Automatic detection of non-apnea arousals may help us better understand various causes of sleep disturbance and advance sleep arousal analysis . Neural networks possess powerful feature-learning abilities to gain insights from complex datasets . Previous studies and guidelines have suggested that sleep arousals are linked mainly to abrupt frequency shifts in EEG signals , but the proposed rules are shown to be insufficient for a comprehensive characterization of arousals . This study investigates the application of five recent convolutional neural networks ( CNNs ) for sleep arousal detection and performs comparative evaluations to determine the best model for this task . The investigated state-of-the-art CNN models have originally been designed for image or speech processing . In comparison , the average AUC and ACC of a Support Vector Machine ( SVM ) based model were 0.69 and 0.67 respectively , and the best AUC from among the same 50 datasets used was 0.88 . This indicates that the proposed CELM based model works well for the sleep arousal detection problem . Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders . High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders . Currently , sleep arousals are mainly annotated by human experts through looking at millions of data points manually , which requires considerable time and effort . However , this method is intrusive and involved a lot of costs . Therefore , this research is proposed to develop an automatic and non-intrusive method of sleep quality assessment for OSA patients . The proposed method used only ECG signal that easy to conduct and record . Multiple methods have been proposed to address the estimation of sleep quality , and devices were developed to conduct the examination in the subject ’s home . The objective of this paper is to analyze the methods and the devices presented in the literature , assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding , leading to developments in the treatment of sleep quality deficits . A systematic review was conducted , selecting research articles published from 2000 to 2018 , and two research questions were formulated , specifically , “ what methods for sleep quality assessment have been developed ” and “ what kind of measures are employed by the devices that have been developed to estimate sleep quality . ” This paper designs and implements a smart pension system based on the Internet of Things . Via a wrist device , our system monitors the wearer ’s health status in real-time , achieves accurate positioning , and implements functions such as fall alarm , motion recording , and sleep quality monitoring . The localization aspect incorporates compressed sensing technology , which improves the positioning accuracy and solves the shortcomings of the traditional localization algorithm . In most cases , the quality of the signal obtained from bed sheet electrodes is good enough for the computer-assisted cardiac arrhythmia detection . Applications for EKG derived RR-interval data include the calculation of HRV parameters that can be utilized in sleep quality analysis and other wellness-related topics as well as sleep apnoea detection . In this work , a novel system ( method ) for sleep quality analysis is proposed . Its purpose is to assist an alternative non-contact method for detecting and diagnosing sleep related disorders based on acoustic signal processing . However , due to the reality gap con- stituted with device performance , subject groups , experiment settings and controlled conditions , the models trained solely on laboratory data can not generalize well to real scenarios . In this work , we investigate the sleep quality estimation for high-speed train drivers as an instance of real-scenario application . Domain adaptation models are adopted to deal with the individual differences across subjects when modeling and testing with the real-scenario data . These technologies offer rich functions ranging from sleep pattern tracking to smart alarm clock . However , previous study indicates that users find these technologies of little use in facilitating sleep quality improvement , as simply making a user aware of how poor his/her sleep is provides no actionable information on how to improve it . Armed with such understanding , we proposed an architecture for designing intelligent sleep analysis systems and developed a prototype called SleepExplorer to help users automatically analyse and visualize the interrelationship of his/her sleep quality and the context ( i.e. , psychological states , physiological states , lifestyle , and environment ) . Quality sleep is essential for human health . Recent developments in sleep monitoring techniques show great promise of detecting sleep disorders and improving sleep quality . Recently , WISP tags , one latest extension of RFID tags , have been used to identify indoor activity recognition , vital signs , sleep quality , and in other health status monitoring systems . It has also been used in many assisted environments or elderly care systems . However , it is obtrusive , requires qualified technicians , and is time and cost expensive . With the introduction of commercial off-the-shelf technologies in the medical field , alternatives to the conventional methods have been conceived to ensure sleep stages and sleep quality detection , which may be now used at home on several nights . Cardio respiratory and physical activities abide the most promising physiological measurements to detect sleep stages without complete PSG . This paper presents the literature study about internet of things for sleep quality monitoring systems . The study is started from the review of sleep quality problem , the importance of sleep quality monitoring , the enabling internet of things technology , and the open issues in this field . Finally , our future research plan for sleep apnea monitoring is presented . Such episodes of unresponsiveness are of particularly high importance in people who perform high-risk occupations requiring extended and unimpaired visuomotor performance , such as truck drivers , train drivers , pilots , and air-traffic controllers , where microsleeps can , and do , result in catastrophic accidents and fatalities . Therefore , prediction and even early detection of microsleeps has the potential to prevent sleep-related accidents and save lives . The aim of this study was to investigate Bayesian methods in the prediction of microsleeps from the EEG . Bayesian methods were also investigated as a measure of improving performance in microsleep detection . This study used data from a previous study , ‘ Study A ’ , in which 15 healthy non-sleep-deprived participants performed two 1-h sessions of 1-D continuous tracking task ( CTT ) . A previous paper [ 1 ] describes a filter feature selection method , ADEN ( Average Distance between Events and Nonevents ) , which outperformed PCA for a simulated EEG feature-selection task . In this paper we describe the application of a single-variable classifier filter method , referred to here as discriminative feature ranking ( DFR ) , which outperforms ADEN and another filter method , mutual information ( MI ) , at selecting relevant features for microsleep detection on real EEG . DFR internally trains and validates a classifier for each feature , which contrasts it with statistical filter methods , including ADEN and MI . From both types of measurements the Power Spectral Densities were estimated and were subsequently reduced by five different methods . Their performances were evaluated empirically in order to get lowest errors for the detection of microsleep events . The detection was realized by Computational Intelligence methods . A software framework is described which was designed to process EEG , video of one eye , and head movement in real time , towards achieving early detection of microsleeps for prevention of fatal accidents , particularly in transport sectors . The framework is based around a pipeline structure with user-replaceable signal processing modules . Cross validated classification accuracies ranged between 92.2 % for ( i , b ) and 99.3 % for ( ii , a ) . It is concluded that expert evaluations are very important to provide independent information for detecting microsleep . Recently , aided by the miniaturization and reduced cost of headsets , a strong interest in real-time EEG signal analysis for classification and prediction has emerged , in domains such as gaming , and security at work . Research , lead at the New Zealand brain Research Institute and University of Canterbury , targets the development of an EEG headset for the detection of microsleeps . In this work , we propose a non-intrusive driver hypovigilance detection system in real-time . This system makes it possible to detect drowsiness by the identification of MicroSleep corresponding to a sleepiness of more than 2 seconds through the analysis of eye-closure , and to identify fatigue by the analysis of the movement of the mouth to detect yawning . In case of drowsiness or fatigue , an alert is launched to make the driver vigilant and thus definitely avoid road accidents , decrease the percentage of murders and injuries caused by driver hypovigilance , then save many human lives . A novel approach for Microsleep Event detection is presented . This is achieved based on multisensor electroencephalogram ( EEG ) and electrooculogram ( EOG ) measurements recorded during an overnight driving simulation task . : sleep quality prediction with mobile phone . Understanding the relationship between sleep and daily life can provide insights into a healthy life style since the sleep quality is one of the most important indicators of people 's health status . The sleep quality prediction has implications beyond trivial . It enables the holistic management of the clinical ramifications of treating sleep disorders , which include providing a foundational framework for mitigating sleep medication abuse and sleep medication dosage control due to the foreknowledge of the quality of a future sleep episode . Sleep Quality ( SQ ) is presented as a function of sleep stages and as such , predicting sleep quality will involve predicting the future realization of a sleep episode in terms of transition between different sleep stages . Electrocardiograms ( ECG ) provided by the National Sleep Research Resource ( NSRR ) are analyzed and a Sleep Quality ( SQ ) value is predicted on an interval ( 0,1 ) . Main aim of this study is to present another , easier and cheaper way to detect SAS . Proposed method is based on prediction of sleep state using only oximetry and heart rate . The Artificial Neural Network ( ANN ) algorithm to predict time series was introduced . To design intelligent sleep management tools , such as the music-induce sleep-aid device , automatic detection of sleep onset is critical . In this work , we propose a simple yet accurate method for sleep onset prediction , which merely relies on Electroencephalogram ( EEG ) signal acquired from a single frontal electrode in a wireless headband . The proposed method first extracts energy power ratio of theta ( 4 - 8Hz ) and alpha ( 8 - 12Hz ) bands along a 3-second shifting window , then calculates the slow wave of each frequency band along the time domain .
A distinctive peculiarity of this database is that it contains the annotations for each minute of each recording indicating the presence or absence of apnea at the current moment of time . To evaluate the effectiveness of ECG derived parameters for sleep apnea detection , 80 ECG segments of 10 minutes duration , annotated as apnea , and 73 ECG segments of the same duration , annotated as normal sleeping were extracted and investigated . The purpose of this work is to define and compare the informative features for identifying episodes of sleep apnea in ECG by heart rate variability analysis , as well as to choose the classification method that provides the highest accuracy for this task .
In this work , we apply machine learning to investigate the effectiveness of an Impulse Radio Ultra-Wide Band ( IR-UWB ) radar panel , in an under-the-mattress configuration , for detecting apnea events in subjects known to have obstructive sleep apnea ( OSA ) . We consider a collection of features , some novel and some inspired by features that worked well for sleep apnea detection using other types of sensors ( i.e. , not IR-UWB ) . To extract the features , we collected a total of 25 hours of data from four subjects as they slept through the night .
Abstract We propose a method based on perceptual prosodic features for medium term speaker state classification , particularly sleepiness detection . Unlike existing methods , our features represent spectral characteristics of speech in perceptual bands and also track temporal content omitting any linguistic segmentation .
the author took the fifth minute data so that there were 12 data generated , out of the 12 data there were several subject conditions which were drowsy and not . From the results of sleepiness detection , get the data which will then be classified using fuzzy tsukamoto and get drowsy data with results 50 .
Each recording was then analyzed offline by a PSG technologist via visual observation of PSG waveforms , who annotated sleep stages N1 and N2 by using the American Academy of Sleep Medicine ( AASM ) scoring rules . Using this as the gold standard , our approach achieved a 87.5 % accuracy for sleep onset detection . The result is better or at least comparable to the other state of the art methods which use either multi-or single- channel based data .
Early detection of sleep arousal in polysomnographic ( PSG ) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications , including heart disease and blood pressure fluctuations . In this paper , we present a new automatic detector of non-apnea arousal regions in multichannel PSG recordings .
Sleep arousal is a common health problem that negatively affects the quality of sleep . This study investigates the use of a compact convolutional neural network ( CNN ) to classify apnea and non-apnea sleep arousal categories .
The goal is to assign a probability of arousal at each sample for each test subject . Automatic detection of non-apnea arousals may help us better understand various causes of sleep disturbance and advance sleep arousal analysis . Neural networks possess powerful feature-learning abilities to gain insights from complex datasets .
Previous studies and guidelines have suggested that sleep arousals are linked mainly to abrupt frequency shifts in EEG signals , but the proposed rules are shown to be insufficient for a comprehensive characterization of arousals . This study investigates the application of five recent convolutional neural networks ( CNNs ) for sleep arousal detection and performs comparative evaluations to determine the best model for this task . The investigated state-of-the-art CNN models have originally been designed for image or speech processing .
In comparison , the average AUC and ACC of a Support Vector Machine ( SVM ) based model were 0.69 and 0.67 respectively , and the best AUC from among the same 50 datasets used was 0.88 . This indicates that the proposed CELM based model works well for the sleep arousal detection problem .
Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders . High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders . Currently , sleep arousals are mainly annotated by human experts through looking at millions of data points manually , which requires considerable time and effort .
However , this method is intrusive and involved a lot of costs . Therefore , this research is proposed to develop an automatic and non-intrusive method of sleep quality assessment for OSA patients . The proposed method used only ECG signal that easy to conduct and record .
Multiple methods have been proposed to address the estimation of sleep quality , and devices were developed to conduct the examination in the subject ’s home . The objective of this paper is to analyze the methods and the devices presented in the literature , assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding , leading to developments in the treatment of sleep quality deficits . A systematic review was conducted , selecting research articles published from 2000 to 2018 , and two research questions were formulated , specifically , “ what methods for sleep quality assessment have been developed ” and “ what kind of measures are employed by the devices that have been developed to estimate sleep quality . ”
This paper designs and implements a smart pension system based on the Internet of Things . Via a wrist device , our system monitors the wearer ’s health status in real-time , achieves accurate positioning , and implements functions such as fall alarm , motion recording , and sleep quality monitoring . The localization aspect incorporates compressed sensing technology , which improves the positioning accuracy and solves the shortcomings of the traditional localization algorithm .
In most cases , the quality of the signal obtained from bed sheet electrodes is good enough for the computer-assisted cardiac arrhythmia detection . Applications for EKG derived RR-interval data include the calculation of HRV parameters that can be utilized in sleep quality analysis and other wellness-related topics as well as sleep apnoea detection .
In this work , a novel system ( method ) for sleep quality analysis is proposed . Its purpose is to assist an alternative non-contact method for detecting and diagnosing sleep related disorders based on acoustic signal processing .
However , due to the reality gap con- stituted with device performance , subject groups , experiment settings and controlled conditions , the models trained solely on laboratory data can not generalize well to real scenarios . In this work , we investigate the sleep quality estimation for high-speed train drivers as an instance of real-scenario application . Domain adaptation models are adopted to deal with the individual differences across subjects when modeling and testing with the real-scenario data .
These technologies offer rich functions ranging from sleep pattern tracking to smart alarm clock . However , previous study indicates that users find these technologies of little use in facilitating sleep quality improvement , as simply making a user aware of how poor his/her sleep is provides no actionable information on how to improve it . Armed with such understanding , we proposed an architecture for designing intelligent sleep analysis systems and developed a prototype called SleepExplorer to help users automatically analyse and visualize the interrelationship of his/her sleep quality and the context ( i.e. , psychological states , physiological states , lifestyle , and environment ) .
Quality sleep is essential for human health . Recent developments in sleep monitoring techniques show great promise of detecting sleep disorders and improving sleep quality .
Recently , WISP tags , one latest extension of RFID tags , have been used to identify indoor activity recognition , vital signs , sleep quality , and in other health status monitoring systems . It has also been used in many assisted environments or elderly care systems .
However , it is obtrusive , requires qualified technicians , and is time and cost expensive . With the introduction of commercial off-the-shelf technologies in the medical field , alternatives to the conventional methods have been conceived to ensure sleep stages and sleep quality detection , which may be now used at home on several nights . Cardio respiratory and physical activities abide the most promising physiological measurements to detect sleep stages without complete PSG .
This paper presents the literature study about internet of things for sleep quality monitoring systems . The study is started from the review of sleep quality problem , the importance of sleep quality monitoring , the enabling internet of things technology , and the open issues in this field . Finally , our future research plan for sleep apnea monitoring is presented .
Such episodes of unresponsiveness are of particularly high importance in people who perform high-risk occupations requiring extended and unimpaired visuomotor performance , such as truck drivers , train drivers , pilots , and air-traffic controllers , where microsleeps can , and do , result in catastrophic accidents and fatalities . Therefore , prediction and even early detection of microsleeps has the potential to prevent sleep-related accidents and save lives . The aim of this study was to investigate Bayesian methods in the prediction of microsleeps from the EEG . Bayesian methods were also investigated as a measure of improving performance in microsleep detection . This study used data from a previous study , ‘ Study A ’ , in which 15 healthy non-sleep-deprived participants performed two 1-h sessions of 1-D continuous tracking task ( CTT ) .
A previous paper [ 1 ] describes a filter feature selection method , ADEN ( Average Distance between Events and Nonevents ) , which outperformed PCA for a simulated EEG feature-selection task . In this paper we describe the application of a single-variable classifier filter method , referred to here as discriminative feature ranking ( DFR ) , which outperforms ADEN and another filter method , mutual information ( MI ) , at selecting relevant features for microsleep detection on real EEG . DFR internally trains and validates a classifier for each feature , which contrasts it with statistical filter methods , including ADEN and MI .
From both types of measurements the Power Spectral Densities were estimated and were subsequently reduced by five different methods . Their performances were evaluated empirically in order to get lowest errors for the detection of microsleep events . The detection was realized by Computational Intelligence methods .
A software framework is described which was designed to process EEG , video of one eye , and head movement in real time , towards achieving early detection of microsleeps for prevention of fatal accidents , particularly in transport sectors . The framework is based around a pipeline structure with user-replaceable signal processing modules .
Cross validated classification accuracies ranged between 92.2 % for ( i , b ) and 99.3 % for ( ii , a ) . It is concluded that expert evaluations are very important to provide independent information for detecting microsleep .
Recently , aided by the miniaturization and reduced cost of headsets , a strong interest in real-time EEG signal analysis for classification and prediction has emerged , in domains such as gaming , and security at work . Research , lead at the New Zealand brain Research Institute and University of Canterbury , targets the development of an EEG headset for the detection of microsleeps .
In this work , we propose a non-intrusive driver hypovigilance detection system in real-time . This system makes it possible to detect drowsiness by the identification of MicroSleep corresponding to a sleepiness of more than 2 seconds through the analysis of eye-closure , and to identify fatigue by the analysis of the movement of the mouth to detect yawning . In case of drowsiness or fatigue , an alert is launched to make the driver vigilant and thus definitely avoid road accidents , decrease the percentage of murders and injuries caused by driver hypovigilance , then save many human lives .
A novel approach for Microsleep Event detection is presented . This is achieved based on multisensor electroencephalogram ( EEG ) and electrooculogram ( EOG ) measurements recorded during an overnight driving simulation task .
: sleep quality prediction with mobile phone . Understanding the relationship between sleep and daily life can provide insights into a healthy life style since the sleep quality is one of the most important indicators of people 's health status .
The sleep quality prediction has implications beyond trivial . It enables the holistic management of the clinical ramifications of treating sleep disorders , which include providing a foundational framework for mitigating sleep medication abuse and sleep medication dosage control due to the foreknowledge of the quality of a future sleep episode . Sleep Quality ( SQ ) is presented as a function of sleep stages and as such , predicting sleep quality will involve predicting the future realization of a sleep episode in terms of transition between different sleep stages . Electrocardiograms ( ECG ) provided by the National Sleep Research Resource ( NSRR ) are analyzed and a Sleep Quality ( SQ ) value is predicted on an interval ( 0,1 ) .
Main aim of this study is to present another , easier and cheaper way to detect SAS . Proposed method is based on prediction of sleep state using only oximetry and heart rate . The Artificial Neural Network ( ANN ) algorithm to predict time series was introduced .
To design intelligent sleep management tools , such as the music-induce sleep-aid device , automatic detection of sleep onset is critical . In this work , we propose a simple yet accurate method for sleep onset prediction , which merely relies on Electroencephalogram ( EEG ) signal acquired from a single frontal electrode in a wireless headband . The proposed method first extracts energy power ratio of theta ( 4 - 8Hz ) and alpha ( 8 - 12Hz ) bands along a 3-second shifting window , then calculates the slow wave of each frequency band along the time domain .
Identifying episodes of sleep apnea in ECG by machine learning methods Towards Sleep Apnea Screening with an Under-the-Mattress IR-UWB Radar Using Machine Learning Medium term speaker state detection by perceptually masked spectral features PERANCANGAN SISTEM DETEKSI KANTUK MENGGUNAKAN METODEARTIFIAL INTELEGEN (AI) BERBASIS NEUROSKY MINDWAVE DANSMSGATEWAY Automatic sleep onset detection using single EEG sensor Hybrid scattering-LSTM networks for automated detection of sleep arousals. Classification of Sleep Arousal using Compact CNN Effectiveness of a Convolutional Neural Network in Sleep Arousal Classification Using Multiple Physiological Signals 1D Convolutional Neural Network Models for Sleep Arousal Detection Automatic Sleep Arousal Detection Based on C-ELM DeepSleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning Automatic Sleep Quality Assessment for Obstructive Sleep Apnea Patients Based on HRV Spectrum Analysis A Review of Approaches for Sleep Quality Analysis A Design and Implementation of an Intelligent Care System for the Elderly Based on Internet of Things Night-Time EKG and HRV Monitoring With Bed Sheet Integrated Textile Electrodes Sleep-quality assessment from full night audio recordings of sleep apnea patients Sleep Quality Estimation with Adversarial Domain Adaptation: From Laboratory to Real Scenario Designing Intelligent Sleep Analysis Systems for Automated Contextual Exploration on Personal Sleep-Tracking Data Design and Implementation of a Noncontact Sleep Monitoring System Using Infrared Cameras and Motion Sensor MonAC: detecting missing tags for improved accuracy in computational RFID based assisted environments Unobtrusive Sleep Monitoring Using Cardiac, Breathing and Movements Activities: An Exhaustive Review Internet of things for sleep quality monitoring system: A survey Prediction of microsleeps from EEG using Bayesian approaches. A Comparison of feature ranking metrics for microsleep detection from the EEG Feature reduction for microsleep detection A software framework for real-time multi-modal detection of microsleeps The role of expert evaluation for microsleep detection Multi-reference adaptive noise cancellation in the EEG Real-Time Driver's Hypovigilance Detection using Facial Landmarks Fusion of State Space and Frequency- Domain Features for Improved Microsleep Detection Will you have a good sleep tonight?: sleep quality prediction with mobile phone Statistical Learning Models for Sleep Quality Prediction Using Electrocardiograms Estimation of Apnea-Hypopnea Index in Sleep Breathing Disorders with the Use of Artificial Neural Networks Automatic sleep onset detection using single EEG sensor
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