Automatic Obstructive Sleep Apnea Detection Research Articles

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.

Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach. PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA. Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.

Semi-Supervised Learning for Low-cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram.

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can lead to a wide range of health issues if left untreated. This study aims to address the lack of research on personalized models for single-lead electrocardiogram (ECG)-based OSA detection, by proposing an automatic semi-supervised algorithm for automated low-cost personalization fine-tuning. We utilize a convolutional neural network (CNN)-based auto-encoder (AE) with a modified training objective to detect anomalous region of OSA. An indicator based on model outputs is utilized as a benchmark measure to assign pseudo-labels with confidence to each sample. Finally, we perform validation of the semi-supervised algorithm on the same database and cross-database scenarios. By introducing semi-supervised personalization, the accuracy, AUC, and mean absolute error (MAE) of the general model (GM) of 35 subjects from the same database are improved from 86.3%, 0.915, and 5.178 to 90.3%, 0.948, and 2.593. Simultaneously, in the validation of 25 subjects from a cross-database, the accuracy, AUC, and MAE of the GM are enhanced from 75.6%, 0.800, and 9.149 to 84.3%, 0.881, and 3.509. The improved version of AE demonstrates excellent adaptability in identifying abnormal features in OSA, employing a data-driven approach to assign pseudo-labels for unknown data automatically. Additionally, leveraging the pseudo-labels through a semi-supervised fine-tuning strategy provides a solution to overcome the limitation of clinical annotations, facilitating low-cost implementation of personalized models. The semi-supervised approach proposed in this paper provides a high-performance and annotation-free solution for personalized adjustment of automatic OSA detection.

Comparison of ALEXNET and GOOGLENET convolutional neural network models to detect obstructive sleep apnea using single-channel electrocardiogram

Obstructive Sleep apnea (OSA) is a type of sleep disorder caused due to respiratory collapse during sleep. This sleep disorder generally goes undiagnosed and neglected. Severe OSA may cause arrhythmia, sudden death, high blood pressure, and other cardiac anomalies. Polysomnography (PSG) is the most popular gold standard used by many researchers to detect OSA. PSG required a well-equipped sleep laboratory and skilled persons to record multi- channel signals to detect OSA. PSG is a complex and expensive method and hence motivated to conduct the research using single-channel electrocardiogram (ECG). An automatic detection method of OSA using single-channel ECG in Convolutional Neural Network (CNN) takes less computing time as feature engineering does not require. This paper focuses on the automatic detection of OSA using ECG with two different deep CNN architectures AlexNet and GoogLeNet transfer learning. The apnea ECG datasheet is used for assessing the method proposed. The state of art using deep learning models are applied to single-channel ECG data. The GoogLeNet architecture is more complex and achieves 100% accuracy whereas AlexNet architecture shows 99.7% accuracy to detect OSA. The proposed work is applied to physionet apnea ECG online data which leads to an overfitting problem that can be resolved using clinical data to further enhance the robustness of the model.

Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture

Obstructive sleep apnea (OSA) is a sleep breathing disorder that can seriously affect the health of patients. The manual diagnostic of OSA through the Polysomnography (PSG) recordings is time-consuming and tedious. Electrocardiogram (ECG) signals have been an alternative for OSA detection. This paper proposes a CNN-Transformer architecture for automatic OSA detection based on single-channel ECG signals. The proposed architecture has two fundamental parts. The first part has the aim of learning a feature representation from ECG signals by using the CNN. The second part consists mainly of the Transformer, a model structure built solely with self-attention mechanism, which is used to model the global temporal context and to perform classification tasks. The effectiveness of the proposed method was validated on Apnea-ECG dataset. The dataset consists of 70 ECG recordings with an annotation for each minute of each recording. The current and adjacent 1-min epochs were combined to form the 3-min input epoch. Besides, experiments were set up with different baseline deep learning models for sequence modeling to verify their effects on classification performance. The per-segment classification accuracy reached 88.2% and the area under the receiver operating characteristic curve (AUC) was 0.95. The per-recording classification accuracy reached 100% and the mean absolute error (MAE) was 4.33. Experimental results demonstrate that the Transformer structure and a 3-min input time window both effectively improve the classification performance. The proposed method can accurately detect OSA from single-channel ECG signals and provides a promising and reliable solution for home portable detection of OSA.

Multiple-instance learning for EEG based OSA event detection

Obstructive sleep apnea (OSA) is a common sleep disease which may cause many serious health problems, therefore timely diagnosis and treatment could bring important help for patients. The current research on automatic detection through bioelectric signal mainly rely on blood oxygen level (SpO2), ECG signal and airflow, with few EEG based solution proposed. More importantly, most researches on OSA detection did not realize the difference between OSA detection and the general sleep staging classification. Some of them just simply transfer sleep staging methods to OSA detection, and an EEG segment was processed as a whole, ignoring the ambiguity in feature space that some EEG frames may contain both normal fragments and OSA fragments. In this paper, we propose a framework, EEG multi-instance learning network (EEG-MIL) for automatic OSA detection based on EEG signals to alleviate this ambiguity. EEG-MIL is composed of subframe multi-resolution convolution extractor (S-MRCNN) and MIL mapping function, which could extract features from sub-frames and mine the interactive relationship between different instances (sub-frames) and bags (frame) to further distinguish OSA event fragment. Meanwhile, in order to meet the clinic needs, we define instance-level task and bag-level task in OSA events detection, and redefine the evaluation criteria to evaluate the effect of our model more comprehensively. Then we verify the performance of our framework in two public datasets and the private dataset, and provide detailed ablation experiments. We validate our method via 5-fold subject independent cross validation approach. Our model obtains 2–8.6% performance gain compared to other works and achieves the new state-of-the-arts.

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals

A novel framework for automatic detection of obstructive sleep apnea (OSA) is introduced in which a symbolic dynamics method, alphabet entropy, along with other well-known features such as fuzzy/approximate and sample entropy are calculated from ECG-derived respiration (EDR) and heart rate variability (HRV) signals. In addition, six different algorithms are employed in the extraction of the EDR signal from a single-lead ECG, and the results are compared. The sequential feature selection method is applied to pick the most effective features. Finally, the picked features are fed into the different classifiers to classify OSA patients and normal subjects. The Physionet Apnea-ECG and Fantasia datasets are utilized to assess the proposed OSA detection method and EDR extraction algorithms, respectively. The results show that the GentleBoost classifier has achieved the accuracy of 93.26% and 100% in per-segment and per-recording classifications, respectively. The proposed automatic OSA detection system outperforms other existing state-of-the-art methods in per-segment classification.

Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability.

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on physiological signals. It has been reported that OSA is, along with autonomic nervous system (ANS) dysfunction and heart rate variability (HRV), a useful tool for ANS assessment. Therefore, in this paper, eight novel indices of short-time HRV are extracted for OSA detection, which are based on the proposed multi-bands time-frequency spectrum entropy (MTFSE) method. In the MTFSE, firstly, the power spectrum of HRV is estimated by the Burg–AR model, and the time-frequency spectrum image (TFSI) is obtained. Secondly, according to the physiological significance of HRV, the TFSI is divided into multiple sub-bands according to frequency. Last but not least, by studying the Shannon entropy of different sub-bands and the relationships among them, the eight indices are obtained. In order to validate the performance of MTFSE-based indices, the Physionet Apnea–ECG database and K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) classification methods are used. The SVM classification method gets the highest classification accuracy, its average accuracy is 91.89%, the average sensitivity is 88.01%, and the average specificity is 93.98%. Undeniably, the MTFSE-based indices provide a novel idea for the screening of OSA disease.

Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal

Obstructive sleep apnea (OSA) is a prevalent sleep disorder and highly affects the quality of human life. Currently, gold standard for OSA detection is polysomnogram. Since this method is time consuming and cost inefficient, practical systems focus on the usage of electrocardiogram (ECG) signals for OSA detection. In this paper, a novel automatic OSA detection method using a single-lead ECG signal has been proposed. A nonlinear feature extraction using wavelet transform (WT) coefficients obtained by an ECG signal decomposition is employed. In addition, different classification methods are investigated. ECG signals are decomposed into eight levels using a Symlet function as a mother Wavelet function with third order. Then, the entropy-based features including fuzzy/approximate/sample/correct conditional entropy as well as other nonlinear features including interquartile range, mean absolute deviation, variance, Poincare plot, and recurrence plot are extracted from WT coefficients. The best features are chosen using the automatic sequential forward feature selection algorithm. In order to assess the introduced method, 95 single-lead ECG recordings are used. The support vector machine classifier having a radial basis function kernel leads to an accuracy of 94.63% (sensitivity: 94.43% and specificity: 94.77%) and 95.71% (sensitivity: 95.83% and specificity: 95.66%) for minute-by-minute and subject-by-subject classifications, respectively. The results show that applying entropy-based features for extracting hidden information of the ECG signals outperforms other available automatic OSA detection methods. The results indicate that a highly accurate OSA detection is attained by just exploiting the single-lead ECG signals. Furthermore, due to the low computational load in the proposed method, it can easily be applied to the home monitoring systems.

Sleep Apnea Detection from Single-Lead ECG Using Features Based on ECG-Derived Respiration (EDR) Signals

Background and objectiveOne of the important applications of non-invasive respiration monitoring using ECG signal is the detection of obstructive sleep apnea (OSA). ECG-derived respiratory (EDR) signals, contribute to useful information about apnea occurrence. In this paper, two EDR extraction methods are proposed, and their application in automatic OSA detection using single-lead ECG is investigated. MethodsEDR signals are extracted based on new respiration-related features in ECG beats morphology, such as ECG variance (EDRVar) and phase space reconstruction area (EDRPSR). After evaluating the EDRs by comparing them to a reference respiratory signal, they are used in an automatic OSA detection application. Fantasia and Apnea-ECG database from PhysioNet are used for EDRs assessments and OSA detection, respectively. The final performance of our OSA detection is tested on an independent test data which is also compared with results of other techniques in the literature. ResultsThe extracted EDRs, EDRVar and EDRPSR show correlations of 72% and 70% with reference respiration, which outperform the other state-of-the-art EDR methods. After feature extraction from EDRs and RR intervals series, the combination of RR and EDRPSR feature sets achieved 100% accuracy in subject-based apnea detection on independent test data, and also minute-based apnea detection is done with accuracy, sensitivity and specificity of 90.9%, 89.6% and 91.8%, which is better than other automatic algorithms in the literature. ConclusionsOur OSA detection system using EDRs features yields better independent test results compared with other state-of-the-art automatic apnea detection methods. The results indicate that ECG-based OSA detection system can classify OSA events with high accuracy and suggest a promising, non-invasive and efficient method for apnea detection.

An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals.

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis. In this study, we propose a novel OSA detection approach based on ECG signals by considering temporal dependence within segmented signals. A discriminative hidden Markov model (HMM) and corresponding parameter estimation algorithms are provided. In addition, subject-specific transition probabilities within the model are employed to characterize the subject-to-subject differences of potential OSA patients. To validate our approach, 70 recordings obtained from the Physionet Apnea-ECG database were used. Accuracies of 97.1% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. Compared with other existing methods that simply ignore the temporal dependence of signals, the proposed HMM-based detection approach delivers more satisfactory detection performance and could be extended to other disease diagnosis applications.