Automated Detection Of Sleep Apnea Research Articles

Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.

The online version contains supplementary material available at 10.1007/s13534-023-00297-5.

Automated sleep apnea detection in pregnant women using wavelet-based features

Sleep apnea is a severe sleep disorder that degrades the quality of sleep and causes patients to feel restless and fatigued even after a full night's sleep. Sleep-disordered breathing (SDB) research has grown its importance in recent decades, owing to patients' substantial health effects and poor quality of life. Obstructive sleep apnea (OSA), the most prevalent kind of sleep disorder, that is caused due to a partial blockage in the nasal airway, resulting in low oxygen levels in the patient's blood. The drop in oxygen level may cause high blood pressure, which can lead to major cardiac issues. Further, OSA is also a risk for pregnancy complications in first-time pregnant women (nulliparous pregnancy), as emerging research and studies suggest a strong relationship between sleep-disordered breathing patients and critical pregnancy outcomes like preeclampsia, preterm birth, gestational diabetes, and intrauterine growth retardation.Polysomnography (PSG) recordings with multiple-channel various modalities signals are often used to detect sleep problems. This study aims to detect sleep apnea in nulliparous pregnant women using a simplified PSG-based system that employed only airflow signals. We used the ‘nulliparous pregnancy outcomes study monitoring mothers-to-be (nuMOM2b)’ database, containing 3012 women subjects for sleep-disordered breathing (SDB) related sub-studies. To the best of our knowledge, this is the first computer-aided diagnostic tool developed to automatically identify sleep apnea in pregnant women. In this work on the detection of OSA, we proposed the use of a single-channel airflow (AF) signal in combination with an orthogonal wavelet filter bank. The L1 − norm features are extracted from wavelet coefficients. The selected features are then fed into various machine learning classifiers to automatically detect apnea. The developed model obtained a highest accuracy of 83.9% with an F1 score of 0.91 using the RUSBoosted tree classifier with ten-fold cross-validation (CV) strategy. The system proposed can be used in a home-based compact clinical setup to detect apnea in pregnant women.

Automated sleep apnea detection in snoring signal using long short-term memory neural networks

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a high incidence disease with serious hazard and potential danger. The polysomnography(PSG) has become the gold standard to diagnose OSAHS. However, the PSG is limited for household use because of its operational complexity, technical nature and high consumption.Currently, Microphone, as a non-contacting tensor, is an alternative method. And researchers devoted to analyze respiratory sound for detecting and evaluating OSAHS patients. In this paper, a classifier based on Long Short-Term Memory (LSTM) is proposed to identify the respiratory event-related snoring from simple snoring. Firstly, we collected the sleep sound of 33 patients and 10 normal people from the hospital. And 4780 abnormal snoring segments and 10,740 normal snoring segments were recorded by Mics. Then Mel-frequency cepstrum coefficients(MFCC), Mel Filter Banks (Fbanks), Short-time Energy and Linear Prediction Coefficient(LPC), representing the different characteristics of snoring, are extracted as characteristic features of snoring.At last, a multi-input model based on LSTM is designed, which can receive various audio features to synthesize information to identify snoring. Compared with single feature network processing, the use of multiple feature coefficients can identify the features of snoring at a fine-grained level. In the experiment, our method could classify respiratory event related snoring and normal snoring at accuracy 95.3%, and the accuracy of the three-category snore related to the severity of OSAHS can reach 81.6%. The recognition results can be used for the auxiliary diagnosis of OSAHS.

Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

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.

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.

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks.

Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this paper, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state of the art. Furthermore, when these predictions are mapped to the apnea-hypopnea index, a considerable improvement in per-patient scoring is achieved over conventional methods. This paper presents a powerful aid for trained staff to quickly diagnose sleep apnea.

An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting

Computerized obstructive sleep apnea detection is necessary to speed-up sleep apnea diagnosis and research and for assisting medical professionals. Moreover, the development of a device to monitor sleep apnea that is low-power and portable, requires a reliable and successful sleep apnea detection scheme. In this article, the problem of automated sleep apnea detection using singe-lead electrocardiogram (ECG) signals has been addressed. At first, segments of ECG signals are decomposed using a data-adaptive signal decomposition scheme, namely- tunable-Q factor wavelet transform (TQWT). Three statistical features are extracted from the TQWT sub-bands and train and test matrices are formed afterwards. These matrices are fed into the classifier to identify non-apneic and apneic ECG signal segments. In this work, a new machine learning algorithm, namely- random under sampling boosting (RUSBoost) is implemented to perform classification. This is for the first time TQWT along with RUSBoost is employed for automatic sleep apnea detection to our knowledge. The overall algorithmic performance of our method is inspected for various values of TQWT parameters. Optimal values of these parameters are investigated and determined. The efficacy and appropriateness of RUSBoost are demonstrated as opposed to the commonly used classification models. The algorithmic performance of our sleep apnea identification scheme is also evaluated against existing detection algorithms in the literature. Experimental outcomes manifest that our sleep apnea identification scheme performs better than the existing works in sensitivity, specificity, and accuracy. It can be anticipated that owing to its use of only one channel of ECG signal, the proposed method will be ideal for device implementation, eliminate the onus of clinicians of analyzing a large bulk of data manually, and expedite sleep apnea diagnosis.

Multi Neural Networks Investigation based Sleep Apnea Prediction

Sleep apnea (SA) is recognized as the most important and common type of sleep disorders with several short term and long term side effects on health and prediction of sleep apnea events before they happened can help to prevent these side effects. There are several studies on automated SA detection but not too much works have been done on prediction of apnea's individual episodes. This paper investigated the application of artificial neural networks (ANNs) to predict sleep apnea. Three types of neural networks were investigated: Elman, RBF and feed-forward back propagation on data from 5 patients. Based on the obtained results, generally on all of experiments the best performance is obtained by the feed-forward neural network with average of Area-Under-Curve (AUC) statistic equal to But this superiority was not hold in all individual experiments and each of neural networks were be able to obtain the best result in some cases. This result showed the necessary of more investigation on methods such as dynamic neural networks selections instead of using a fixed model.