Sleep Apnea Detection Research Articles

Detection of obstructive sleep apnea in young patients suffering from coronary artery disease by performing portable polysomnography studies

BackgroundObstructive sleep apnea (OSA) is known to be an important contributory factor of coronary artery disease (CAD), but the extent of contribution of OSA in young patients suffering from CAD is not known. Thus, with an aim to detect OSA in young patients suffering from CAD by performing portable polysomnography (PSG), the present study was carried out at a tertiary care chest center. MethodsA prospective study was carried out from June 2015 to June 2018, wherein 100 consecutive young (age less than 40 years), non-smoking patients with angiographically confirmed CAD, with no identifiable risk factors for cardiovascular diseases except obesity, were subjected to level 3 portable PSG studies. ResultsOf 100 patients with CAD, 80% had OSA (24% with mild OSA, 28% with moderate OSA, and 28% with severe OSA). Body weight and severity of OSA showed a significant correlation with a P-value of 0.033. SPSS software was used for statistical analysis. The categorical variables were compared using Fischer's exact test. ConclusionThe study detected a significant number of young patients with angiographically confirmed CAD having OSA. A significant correlation was also observed between weight and severity of OSA, suggesting that overweight patients and patients with obesity have higher grades of OSA.

A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals.

People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.

A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram.

Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and a softmax classification layer. Thirty-five released and thirty-five withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database were applied to train the proposed CNN model and validate its accuracy for the detection of the apnea events. The results show that the proposed model achieves 87.9% accuracy, 92.0% specificity, and 81.1% sensitivity for per-minute apnea detection, and 97.1% accuracy, 100% specificity, and 95.7% sensitivity for per-recording classification. The proposed model improves the accuracy of sleep apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches.

Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer.

People with obstructive sleep apnea (OSA) remain undiagnosed because of the lack of easy and comfortable screening tools. Through this study, we aimed to compare the diagnostic accuracy of chest wall motion and cyclic variation of heart rate (CVHR) in detecting OSA by using a single-lead electrocardiogram (ECG) patch with a 3-axis accelerometer. In total, 119 patients who snore simultaneously underwent polysomnography with a single-lead ECG patch. Signals of chest wall motion and CVHR from the single-lead ECG patch were collected. The chest effort index (CEI) was calculated using the chest wall motion recorded by a 3-axis accelerometer in the device. The ability of CEI and CVHR indices in diagnosing moderate-to-severe OSA (apnea-hypopnea index ≥ 15) was compared using the area under the curve (AUC) by using the DeLong test. CVHR detected moderate-to-severe OSA with 52.9% sensitivity and 94.1% specificity (AUC: 0.76, 95% confidence interval: 0.67-0.84, optimal cutoff: 21.2 events/h). By contrast, CEI identified moderate-to-severe OSA with 80% sensitivity and 79.4% specificity (AUC: 0.87, 95% confidence interval: 0.80-0.94, optimal cutoff: 7.1 events/h). CEI significantly outperformed CVHR regarding the discrimination ability for moderate-to-severe OSA (ΔAUC: 0.11, 95% confidence interval: 0.009-0.21, P = .032). For determining severe OSA, the performance of discrimination ability was greater (AUC = 0.90, 95% confidence interval: 0.85-0.95) when combining these two signals. Both CEI and CVHR recorded from a patch-type device with ECG and a 3-axis accelerometer can be used to detect moderate-to-severe OSA. Thus, incorporation of CEI is helpful in the detection of sleep apnea by using a single-lead ECG with a 3-axis accelerometer.

Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search

Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search

Machine learning for automatic identification of thoracoabdominal asynchrony in children.

The current methods for assessment of thoracoabdominal asynchrony (TAA) require offline analysis on the part of physicians (respiratory inductance plethysmography (RIP)) or require experts for interpretation of the data (sleep apnea detection). To assess synchrony between the thorax and abdomen, the movements of the two compartments during quiet breathing were measured using pneuRIP. Fifty-one recordings were obtained: 20 were used to train a machine-learning (ML) model with elastic-net regularization, and 31 were used to test the model's performance. Two feature sets were explored: (1) phase difference (ɸ) between the thoracic and abdominal signals and (2) inverse cumulative percentage (ICP), which is an alternate measure of data distribution. To compute accuracy of training, the model outcomes were compared with five experts' assessments. Accuracies of 61.3% and 90.3% were obtained using ɸ and ICP features, respectively. The inter-rater reliability (i.r.r.) of the assessments of experts was 0.402 and 0.684 when they used ɸ and ICP to identify TAA, respectively. With this pilot study, we show the efficacy of the ICP feature and ML in developing an accurate automated approach to identifying TAA that reduces time and effort for diagnosis. ICP also helped improve consensus among experts. Our article presents an automated approach to identifying thoracic abdominal asynchrony using machine learning and the pneuRIP device. It also shows how a modified statistical measure of cumulative frequency can be used to visualize the progression of the pulmonary functionality along time. The pulmonary testing method we developed gives patients and doctors a noninvasive and easy to administer and diagnose approach. It can be administered remotely, and alerts can be transmitted to the physician. Further, the test can also be used to monitor and assess pulmonary function continuously for prolonged periods, if needed.

A Per-sample Digitized Algorithm for Automatically Detecting Apnea and Hypopnea Events from Airflow and Oximetry.

Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.

ECG-Derived Heart Rate Variability Interpolation and 1-D Convolutional Neural Networks for Detecting Sleep Apnea.

Feature extraction from ECG-derived heart rate variability signal has shown to be useful in classifying sleep apnea. In earlier works, time-domain features, frequency-domain features, and a combination of the two have been used with classifiers such as logistic regression and support vector machines. However, more recently, deep learning techniques have outperformed these conventional feature engineering and classification techniques in various applications. This work explores the use of convolutional neural networks (CNN) for detecting sleep apnea segments. CNN is an image classification technique that has shown robust performance in various signal classification applications. In this work, we use it to classify one-dimensional heart rate variability signal, thereby utilizing a one-dimensional CNN (1-D CNN). The proposed technique resizes the raw heart rate variability data to a common dimension using cubic interpolation and uses it as a direct input to the 1-D CNN, without the need for feature extraction and selection. The performance of the method is evaluated on a dataset of 70 overnight ECG recordings, with 35 recordings used for training the model and 35 for testing. The proposed method achieves an accuracy of 88.23% (AUC=0.9453) in detecting sleep apnea epochs, outperforming several baseline techniques.

Sleep Apnea Event Detection from Sub-frame Based Feature Variation in EEG Signal Using Deep Convolutional Neural Network.

The topic of automatic detection of sleep apnea which is a respiratory sleep disorder, affecting millions of patients worldwide, is continuously being explored by researchers. Electroencephalogram signal (EEG) represents a promising tool due to its direct correlation to neural activity and ease of extraction. Here, an innovative approach is proposed to automatically detect apnea by incorporating local variations of temporal features for identifying the global feature variations over a broader window. An EEG data frame is divided into smaller sub-frames to effectively extract local feature variation within one larger frame. A fully convolutional neural network (FCNN) is proposed that will take each sub-frame of a single frame individually to extract local features. Following that, a dense classifier consisting of a series of fully connected layers is trained to analyze all the local features extracted from subframes for classifying the entire frame as apnea/non-apnea. Finally, a unique post-processing technique is applied which significantly improves accuracy. Both the EEG frame length and post-processing parameters are varied to find optimal detection conditions. Large-scale experimentation is executed on publicly available data of patients with varying apnea-hypopnea indices for performance evaluation of the suggested method.

Embedded system for non-obtrusive sleep apnea detection.

This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment.

Distinguish Obstructive and Central Sleep Apnea by Portable Peripheral Arterial Tonometry.

Sleep apnea has a very high prevalence in the general population. Sleep apnea can be the cause for cardiovascular disorders. An increased risk for suffering from hypertension, stroke, and myocardial infarction had been shown in large studies, like the Sleep Heart Health Study. Sleep related breathing disorders and sleep apnea had been diagnosed in sleep laboratories with polysomnography in the past. Today in view of the high prevalence of sleep disordered breathing, home sleep apnea testing (HSAT) has become the accepted test for the diagnosis of sleep apnea, if there are no other comorbidities, and if a high pretest probability was confirmed by a sleep physician. For home sleep apnea testing, the number of sensors needed should be reduced. Some methods use indirect means to derive features to detect sleep apnea and hypopnea events. A very well developed method is peripheral arterial tonometry (PAT). This method records the pulse wave on a finger and derives sleep and sleep apnea feature. The PAT method has been tested under many conditions. As an indirect method, it was long seen as a limitation that obstructive and central sleep apnea events could not be distinguished. A new multicenter trial was set up to develop algorithms, which could distinguish central and obstructive apnea events with sufficient accuracy.

Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal

Background and objective: This paper addresses the automated recognition of obstructive sleep apnea (OSA) from the analysis of single-lead ECG signals. This is one of the most important problems that is, critical to the realization of monitoring patients with sleep apnea.Methods: In the present study, a novel solution based on autoregressive (AR) modeling of the single-lead ECG, and spectral autocorrelation function as an ECG feature extraction method is presented. The more effective features are opted by sequential forward feature selection (SFFS) technique and fed into the random forest for binary classification between the apnea and normal events.Results: Experimental results on Apnea-ECG database proved that the introduced algorithm resulted in an accuracy of 93.90% (sensitivity of 92.26% and specificity of 94.92%) in per-segment classification, which outperforms the other cutting-edge automatic OSA recognition techniques. Moreover, the proposed algorithm provided an accuracy of 97.14% (sensitivity of 95.65% and specificity of 100%) in discrimination of apnea patients from the normal subjects, which is comparable to the traditional and existing approaches.Conclusions: This study suggests that automatic OSA recognition from single-lead ECG signals is possible, which can be used as an inexpensive and low complexity burden alternative to more conventional methods such as Polysomnography.

Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis

The heartbeat interval (HBI) signal (RR-time series), and electrocardiogram (ECG) derived respiration (EDR) signal quantify the information about the cardiopulmonary activity, and monitoring these two signals simultaneously will provide more information for the sleep apnea detection. This paper proposes a novel approach to detect sleep apnea using both HBI and EDR signals. The approach consists of the decomposition of both HBI and EDR signals into reconstructed components (RCs) or modes using a data-driven signal processing approach namely, the sliding mode singular spectrum analysis (SM-SSA), extraction of features from each RC, and the use of classifier for the detection of sleep apnea. The features such as the mean and the standard deviation values are extracted from the instantaneous amplitude (IA) and instantaneous frequency (IF) of each RC of both HBI and EDR signals. The classifiers, such as the stacked autoencoder based deep neural network (SAE-DNN), and support vector machine (SVM) are considered to classify normal and apnea episodes using the statistical features obtained from the RCs of HBI and EDR signals. The proposed approach is evaluated using different public databases such as apnea-ECG database, University College Dublin (UCD) database, and Physionet challenge database, respectively. The results demonstrate that the combination of the statistical features and SVM classifier has the sensitivity and specificity values of 82.45% and 79.72%, respectively using the 10-fold cross-validation based selection of training and test instances from the apnea-ECG database. Moreover, for subject-specific cross-validation, the proposed method has an average sensitivity and specificity values of 62.87%, and 81.53%, respectively. The proposed method has achieved the accuracy values of 94.3%, and 72% for per-recording based classification of sleep apnea and normal classes using signals from apnea-ECG and UCD databases.

Home sleep apnea testing in the era of COVID-19: a community perspective.

Citation:Kole A. Home sleep testing in the era of COVID-19: a community perspective. J Clin Sleep Med. 2020;16(9):1633.

Deep Learning for Detecting Sleep Apnea from ECG Signals

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.

Detection of apnea events from ECG segments using Fourier decomposition method

Absence of airflow in breathing during sleep for more than 10 s is known as sleep apnea. It causes low oxygen levels in the blood which may lead to many cardiovascular problems. Current methods of detection are rather time-consuming and expensive. Automated detection using electrocardiogram (ECG) signal is seen as a promising and efficient method for the identification of sleep apnea events. In this paper, the single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method. From these signal components, features like mean absolute deviation and entropy are computed to classify the ECG segments using machine learning algorithms. The proposed method yields an accuracy of 92.59%, sensitivity of 89.70%, specificity of 94.67% and precision of 91.27% on MIT PhysioNet Apnea-ECG dataset, using a support vector machine (SVM) classifier with the Gaussian kernel. The strength of the proposed method has been verified on two more datasets, namely MIT-BIH polysomnography and University College Dublin sleep apnea database (UCDDB). The classification results are compared with the existing state-of-the-art techniques to demonstrate the superior performance of the proposed method. Proposed methodology is implemented using the fast Fourier transform (FFT) which makes it computationally efficient and can be used for real-time sleep apnea detection.

0584 Detecting Sleep Disordered Breathing Using Sub-Terahertz Radio-Frequency Micro-Radar

Abstract Introduction New sensor technologies are entering sleep testing at a rapid pace; Neteera™ developed a novel sensor and algorithm for sleep apnea detection utilizing a contact-free, radar-based sensor system. The system utilizes a high-frequency, low-power, directional micro-radar which operates at ~120GHz and a sampling rate of 2500Hz as well as algorithms which are able to detect both pulse and respiratory activity of subjects during sleep. Methods Adult subjects undergoing diagnostic PSG for clinical purposes were simultaneously assessed with the novel micro-radar system with sensors under the mattress. Disordered breathing events (DBEs) were scored from the PSG using AASM scoring guidelines and were compared with those detected by the micro-radar sensor. Test data were grouped into three sets: 1. Single under mattress sensor; 2. Two under mattress sensors on each side of the bed (to improve signal capture); 3. After software optimization. The micro-radar sensor detected DBEs but software to describe the type of DBEs (obstructive apnea/central apnea/hypopnea) is still under development. Detection rate of DBEs was compared between the two methodologies and the development sets. Results n=22 (12 F, 10 M), Age=50.8±12.4 years, BMI=35.32±7.37 kg/m2. Diagnostic PSG AHI: 19.7±29.4/hr, T90=15.8±25.7%. Percent DBEs missed by the micro-radar sensor: 1st set=14.6±10.6%; 2nd set=9.4±8.3%; 3rd set=1.2±2.6%. Number of DBEs assessed for each set was 646, 1144, 125 events, respectively. With each successive set, the detection rate improved. Conclusion A novel micro-radar, non-contact sensor technology can be used to detect DBEs during sleep. Detection rate improved with utilization of two sensors per bed and software optimization. Future software development is expected to improve detection rate and facilitate breathing event classification into obstructive apneas/central apneas/hypopneas. Support None.

ANALYSIS OF OBSTRUCTIVE SLEEP APNEA DISORDER WITH ACCURACY PREDICTION USING SVM FOR SMART ENVIRONMENT

One of the most crucial sleep disorders that has a direct hit on the quality of life is sleep apnea disorder. Declined memory and disorders related to personality are some of the consequences of Sleep Apnea Disorder. Identifying the difference between normal and abnormal levels of snoring sound is important for the detection of sleep apnea. Diagnosis of the sleep apnea usually takes place in hospitals under the direct supervision of medical professionals. Frequent visits to hospital for diagnosis tend to be an inconvenience to the elderly. Along with it, usage of body monitors also acts as a parameter in disrupting sleep. To overcome them, the analysis equipment is placed in the usual sleeping environment of the patient and LM393 sound sensor is used to detect the snoring levels. The analysis between the normal and the acquired snoring levels using threshold values and SVM helps confirm the presence or absence of the sleep apnea disorder.

Early Detection of Obstructive sleep apnea Using Adjusted Neck Circumference Score

Early Detection of Obstructive sleep apnea Using Adjusted Neck Circumference Score

Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time–frequency analysis

Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time–frequency domain analysis of IMFs from CP signal. The time–frequency domain features are computed from the cross time–frequency matrices of IMFs of CP signal. The cross time–frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea.