scispace - formally typeset
Search or ask a question
Journal ArticleDOI

A Comprehensive Analysis on the Efficient Mechanisms to Detect Obstructive Sleep Apnea Using AI and Heuristic Algorithms

01 Jan 2021-International journal of current research and review (Radiance Research Academy)-Vol. 13, Iss: 04, pp 91-96
TL;DR: A comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.
Abstract: Obstructive sleep apnea is a common problem arising in adults and children nowadays, determined by abnormalities in breathing gaps or incapability of air intake capacity during sleeping results in a decrease in oxygen level in blood. The brain detects this sudden decrease in the level of oxygen and sends a signal to wake the person up. Studies revealed the breathing stops for almost 10 seconds during a sleep apnea episode. There is no restriction on who can develop Obstructive Sleep Apnea(OSA), it can affect adults as well as infants. Our research primarily aims at assessing the various recent developments and studies made as a solution to this alarming problem. Their methodology and techniques have been studied and accuracy and sensitivity rates compared. A comprehensive and detailed study has been conducted on several research papers and studies done in the field of predicting sleep apnea. Sleep Apnea and classification of apneic signals have been mentioned in our study. The related researches have been studied extensively and compiled in our research work. The various techniques used by the researchers have been studied and tabulated along with the algorithm accuracies. It is observed that signal measurement along with AI algorithms has made significant advancements in OSA prediction. It is observed that Self Developed Algorithm on VAD showed the highest accuracy of 97%. PPG signal analysis and binary classification algorithm showed good accuracies of 86.67% and 86% respectively. AdaBoost, Decision Table and Bagging REPTree and SVM classifier also showed good accuracy of around 83% in the detection of Sleep Apnea episodes. The study highlighted the research works done to combat the rising problem of Obstructive Sleep Apnea. This comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.

Content maybe subject to copyright    Report

References
More filters
Journal ArticleDOI
TL;DR: The results suggest that PRV can be used in apnea detectors based on DAP events, to discriminate apneic from nonapneic events avoiding the need for ECG recordings.
Abstract: A technique for ambulatory diagnosis of the obstructive sleep apnea syndrome (OSAS) in children based on pulse photoplethysmographic (PPG) signal is presented. Decreases in amplitude fluctuations of the PPG signal (DAP) events have been proposed as OSAS discriminator, since they are related to vasoconstriction associated to apnea. Heart rate variability (HRV) analysis during these DAP events has been proposed to discriminate between DAP events related or unrelated to an apneic event. The use of HRV requires electrocardiogram (ECG) as an additional recording, meaning a disadvantage that takes more relevance in sleep studies context where the number of sensors is tried to be minimized in order not to affect the physiological sleep. This study proposes the use of pulse rate variability (PRV) extracted from the PPG signal instead of HRV. Polysomnographic registers from 21 children (aged 4.47 ±2.04 years) were studied. The subject classification based on DAP events and PRV analysis obtained an accuracy of 86.67% which represents an improvement of 6.67% with respect to the HRV analysis. These results suggest that PRV can be used in apnea detectors based on DAP events, to discriminate apneic from nonapneic events avoiding the need for ECG recordings.

133 citations

Journal ArticleDOI
01 Nov 2009
TL;DR: Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals and estimate the surrogate apnea index (AI) / hypopneaindex (HI) (AHI) and wavelet-based features of 5-s ECGs.
Abstract: Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/hypopnea index (HI) (AHI). Total 82 535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and hypopnea) were classified from normal breathing events, and at the second stage, hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of plusmn 2.19, plusmn 2.16, and plusmn 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals.

127 citations

Journal ArticleDOI
01 May 2012
TL;DR: In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications.
Abstract: Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study, features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for optimizing the classifier with the appropriate features for an OSA automated detection algorithm.

120 citations

Posted Content
TL;DR: It is shown experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application called “BreathTaking”.
Abstract: This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call "BreathTaking". We show that although an individual link cannot reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.3 breaths per minute (bpm) RMS error. Use of directional antennas is shown to improve robustness to motion near the network.

116 citations

Journal ArticleDOI
TL;DR: An innovative signal classification method capable of differentiating subjects with sleep disorders which cause excessive daytime sleepiness (EDS) from normal control subjects who do not have a sleep disorder based on EEG and pupil size is developed.
Abstract: Electroencephalogram (EEG) is able to indicate states of mental activity ranging from concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG energy. In particular, intrusion of EEG theta wave activity into the beta activity of active wakefulness has been interpreted as ensuing sleepiness. Pupil behavior can also provide information regarding alertness. This paper develops an innovative signal classification method that is capable of differentiating subjects with sleep disorders which cause excessive daytime sleepiness (EDS) from normal control subjects who do not have a sleep disorder based on EEG and pupil size. Subjects with sleep disorders include persons with untreated obstructive sleep apnea (OSA) and narcolepsy. The Yoss pupil staging rule is used to scale levels of wakefulness and at the same time theta energy ratios are calculated from the same 2-s sliding windows by Fourier or wavelet transforms. Then, an artificial neural network (NN) of modified adaptive resonance theory (ART2) is utilized to identify the two groups within a combined group of subjects including those with OSA and healthy controls. This grouping from the NN is then compared with the actual diagnostic classification of subjects as OSA or controls and is found to be 91% accurate in differentiating between the two groups. The same algorithm results in 90% correct differentiation between narcoleptic and control subjects.

104 citations