Sleep Apnea Detection Research Articles

Critical Evaluation of Automated Obstructive Sleep Apnea Detection Using EEG and Machine Learning: Strengths, Limitations, and Future Directions

Critical Evaluation of Automated Obstructive Sleep Apnea Detection Using EEG and Machine Learning: Strengths, Limitations, and Future Directions

Automated sleep apnea detection from snoring and carotid pulse signals using an innovative neck wearable piezoelectric sensor

This study introduces an innovative wearable neck piezoelectric sensor (NPS) that measures snoring vibrations and carotid pulsations, offering a significant advancement in sleep apnea syndrome (SAS) diagnosis. Utilizing advanced algorithms like discrete wavelet transform and dynamic thresholding, the NPS detects snoring events with 83% accuracy, comparable to polysomnography, and calculates key metrics such as the snoring index (SI) and normalized snoring vibration energy (SVE%). Unlike traditional methods, the SVE% from NPS directly correlates with subjective assessments of snoring severity. It also measures carotid pulsation metrics such as pulse rate and the standard deviation of normal-to-normal intervals, achieving 85% accuracy in sleep phase determination against polysomnography. Moreover, NPS surpasses traditional methods in SI and SVE% accuracy, closely aligning with clinical evaluations of SAS severity. This user-friendly technology automates the measurement of critical snoring metrics, transforming SAS diagnosis and treatment by enhancing accessibility and efficiency for healthcare providers and patients.

Noncontact Detection of Sleep Apnea Using Radar and Expectation–Maximization Algorithm

Noncontact Detection of Sleep Apnea Using Radar and Expectation–Maximization Algorithm

Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.

Detection of sleep arousal from STFT-based instantaneous features of single channel EEG signal.

Sleep arousal, a frequent interruption in sleep with complete or partial wakefulness from sleep, may indicate a breathing disorder, neurological disorder, or sleep-related disorders. These phenomena necessitate the detection of sleep arousals. Uses of deep learning methods to detect features inhibits the scope to understand the specific distinctive nature of the signals and reduces the interpretability of the model. To evade these inconsistencies and to improve the classification performance of the sleep arousal detection model, a model has been proposed in this study on the prospect of understandable features that are useful in detecting sleep arousals. 
Approach: Time-frequency analysis of the electroencephalogram (EEG) signals was performed using Short-Time Fourier Transform (STFT). From the STFT coefficients, the spectrogram and instantaneous properties (frequency, bandwidth, power spectrum, band energy, local maxima, and band energy ratios) were investigated. From these properties, instantaneous features were generated by statistical analysis. Additive feature sets and reduced feature sets, formed by adding features successively and reducing features using the analysis of variance test respectively, were subjected to a tri-layered neural network classifier to evaluate the capability of the features to detect sleep arousal and normal sleep segments. 
Main results: The reduced feature set (Set 6) has proved to be efficacious in facilitating superior classification performance metrics (accuracy, sensitivity, specificity, and AUC of 89.14%, 83.52%, 89.49%, and 93.84% respectively). 
Significance: This efficient model can be incorporated with an automatic sleep apnea detection system where the estimation of hypopnea requires the detection of sleep arousal.
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Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.

Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.

Hypnos: A Contactless Sleep Stage Monitoring System Using UWB Signals

As a fundamental physiological process, sleep plays a vital role in human health. High-quality sleep requires a reasonable distribution of sleep duration over different sleep stages. Recently, contactless solutions have been used for in-home sleep stage monitoring via wireless signals as it enables monitoring daily sleep in a non-intrusive manner. However, various factors, such as the subject's physiological characteristics during sleep, the subject's health status, and even the sleep environment, pose challenges to wireless signal analysis. In this paper, we propose Hypnos, a contactless sleep monitoring system that identifies different sleep stages using an ultra-wideband (UWB) device. Hypnos enables automated bed localization and extracts signals containing coarse-grained body movements and fine-grained chest movements due to breathing and heartbeat from the subject, which acts as the preparation step for sleep staging. The key to our system is a seq2seq deep learning model, which adopts an attention-based sequence encoder to learn the patterns and transitions within and between sleep epochs and combines with contrastive learning to improve the generalizability of the encoder. Particularly, we incorporate sleep apnea detection as an auxiliary task into the model to reduce the interference of sleep apnea with sleep staging. Moreover, we design a two-step training for better adaptation of subjects with different severities of sleep disorders. We conduct extensive experiments on 100 subjects, including healthy individuals and patients with sleep disorders, and the experimental results show that Hypnos achieves excellent performance in multi-stage sleep classification (including 5-stage sleep classification), and outperforms other baseline methods.

Early detection and treatment of obstructive sleep apnoea in infants with Down syndrome: a prospective, non-randomised, controlled, interventional study

Infants with Down syndrome (DS) are at high risk of obstructive sleep apnoea (OSA) which is associated with neurocognitive dysfunction and behaviour problems. The aim of our study was to evaluate the effect of early OSA treatment in infants with DS on neurocognitive development and behaviour. In this prospective, interventional, non-randomised study, 40 infants with DS underwent polysomnography (PSG) every 6 months in room air between 6 and 36 months of age (Screened Group) and were compared to a control group of 40 infants with DS receiving standard of care and a single, systematic PSG in room air at 36 months of age (Standard Care Group). When present, OSA was treated. The primary endpoint was the total score of the Griffiths Scales of Child Development, Third Edition (Griffiths III) and its subscores at 36 months. Secondary endpoints included a battery of neurocognitive and behaviour questionnaires, and PSG outcomes. On the Griffiths III, the total score was significantly higher in the Screened Group compared to the Standard Care Group (difference: 4.1; 95%CI: 1.3; 7.6; p=0.009). Results in Griffiths III subscores and secondary endpoints were in support of better neurocognitive outcomes in the Screened Group compared with the Standard Care Group. At 36 months, median (Q1; Q3) apnoea-hypopnea index was higher in the Standard Care Group (4.0 [1.5; 9.0] events/hour) compared to the Screened Group (1.0 [1.0; 3.0] events/hour, p=0.006). Moderate and severe OSA were more frequent in the Standard Care Group as compared to the Screened Group (18.9% versus 3.7% for moderate OSA and 27.0% versus 7.4% for severe OSA). Early diagnosis and treatment of OSA in infants with DS may contribute to a significantly better neurocognitive outcome and behaviour at the age of 36 months. The study was funded by the Jérôme Lejeune Foundation.

Obstructive Sleep Apnea Detection from EEG Data: A Hybrid Approach of One-Dimensional Convolutional Neural Network and Enhanced Fuzzy C-Means Clustering Algorithm

Obstructive Sleep Apnea Detection from EEG Data: A Hybrid Approach of One-Dimensional Convolutional Neural Network and Enhanced Fuzzy C-Means Clustering Algorithm

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.

A Systematic Review on Machine Learning / Deep Learning Model-based Detection of Sleep Apnea Using Bio-Signals

Backgrounds: Sleep Apnea (SA) is a sleep-related breathing disorder diagnosed in clinical laboratories. The gold standard is Polysomnography (PSG), a multi-parameter evaluation of a sleep monitoring system that records the biological signals during overnight sleep. Apart from PSG recording, apnea events are recorded by various other bio-signals called Electrocardiogram (ECG), Electroencephalogram (EEG), Oxygen Saturation level (SpO2), etc. Further evaluation of the recorded bio-signals is tedious and time-consuming as experts perform it manually. Aiming to overcome the disadvantage without compromising accuracy, scientists focus on developing robust measurements of SA by using Machine Learning (ML) and Deep Learning (DL) models. Method: This study aimed to analyze the recent research findings in the field of sleep apnea classification and various machine learning and deep learning methods implemented in detecting SA. This study revealed the best-performing technique considering different types of bio-signals used for analysis and the respective ML or DL models used for automatic detection Result: The studies and patents included in this review underwent a precise screening process using PRISMA guidelines. The literature study is comprised of three different analysis tools to showcase the review process and provide evidence for the research findings obtained in the respective publications. The publications considered were limited to the last decade. Conclusion: This review delivers the key finding that ECG signals-based detection of sleep apnea using deep learning model-based deep neural network classifiers will provide more accurate and robust classification, which will pave the way for possible future research directions.

Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model

BackgroundSleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods. MethodThis study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal's dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model's efficacy in identifying SLA occurrences. ResultsThe proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %. ConclusionsThese results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.

MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection

The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.

An Ensemble Learning-Assisted Obstructive Sleep Apnea Detection Model Using EEG Physiological Signals and Improved Extra Tree Classifier

An Ensemble Learning-Assisted Obstructive Sleep Apnea Detection Model Using EEG Physiological Signals and Improved Extra Tree Classifier

Detection and Classification of Obstructive Sleep Apnea Using Audio Spectrogram Analysis

Sleep disorders are steadily increasing in the population and can significantly affect daily life. Low-cost and noninvasive systems that can assist the diagnostic process will become increasingly widespread in the coming years. This work aims to investigate and compare the performance of machine learning-based classifiers for the identification of obstructive sleep apnea–hypopnea (OSAH) events, including apnea/non-apnea status classification, apnea–hypopnea index (AHI) prediction, and AHI severity classification. The dataset considered contains recordings from 192 patients. It is derived from a recently released dataset which contains, amongst others, audio signals recorded with an ambient microphone placed ∼1 m above the studied subjects and apnea/hypopnea accurate events annotations performed by specialized medical doctors. We employ mel spectrogram images extracted from the environmental audio signals as input of a machine-learning-based classifier for apnea/hypopnea events classification. The proposed approach involves a stacked model which utilizes a combination of a pretrained VGG-like audio classification (VGGish) network and a bidirectional long short-term memory (bi-LSTM) network. Performance analysis was conducted using a 5-fold cross-validation approach, leaving out patients used for training and validation of the models in the testing step. Comparative evaluations with recently presented methods from the literature demonstrate the advantages of the proposed approach. The proposed architecture can be considered a useful tool for supporting OSAHS diagnoses by means of low-cost devices such as smartphones.

A Machine Learning Model for Obstructive Sleep Apnea Detection Using Ensemble Learning and Single-Lead EEG Signal Data

A Machine Learning Model for Obstructive Sleep Apnea Detection Using Ensemble Learning and Single-Lead EEG Signal Data

Detection of Sleep Apnea From ECG Signals Using Sliding Singular Spectrum Based Subpattern Principal Component Analysis

Detection of Sleep Apnea From ECG Signals Using Sliding Singular Spectrum Based Subpattern Principal Component Analysis

Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.

To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF). A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least one night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the Apnea-Hypopnea Index (AHI≥5, AHI≥15). Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only forty-eight participants (63%) could be successfully assessed for OSA by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger temperature features to detect moderate-severe sleep apnea (AHI≥15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions. This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes.

A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index.

This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea-hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals.