Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN‐LSTM Model

Zhang, Junming; Zhen, Tang; Gao, Jinfeng; Li, Lin; Liu, Zhiliang; Wu, Haitao; Liu, Fang; Yao, Ruxian

doi:10.1155/2021/5594733

Cited by 28 publications

(18 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Single-channel ECG lead signals are acquired and analyzed using CNN-LSTM network. The Kappa coefficient value of CNN was 0.89, to that of CNN-LSTM network was 0.92 [ 112 ].…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

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.

show abstract

“…Single-channel ECG lead signals are acquired and analyzed using CNN-LSTM network. The Kappa coefficient value of CNN was 0.89, to that of CNN-LSTM network was 0.92 [ 112 ].…”

Section: Classificationmentioning

confidence: 99%

“…The apnea and the normal signals are identified efficiently by CNN-LSTM network. The score transitions between each epoch are considered as a hideous process [ 112 ].…”

Section: Inferences From the Surveymentioning

confidence: 99%

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Then, stacked autoencoder-based deep neural network (SAE-DNN) and support vector machine (SVM) are used to categorize apneic and normal segments. Zhang et al [ 11 ] suggested a sleep monitoring model based on a single-channel electrocardiogram using a convolutional neural network (CNN). Rajesh et al [ 9 ] extracted moments of power spectrum density, waveform complexity measures, and higher-order moments from the 1 min segmented ECG subbands obtained from discrete wavelet transform (DWT).…”

Section: Introductionmentioning

confidence: 99%

“…So, developing methods that can accurately detect apnea with a few signals at home is critical. ese approaches were focused on biosignals such as respiratory, snoring, SpO2, and ECG signals, and several authors have achieved a high level of performance in terms of OSA detection [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, we chose ECG signals to model OSA and normal events in this study. e majority of ECG approaches proposed in the literature for sleep apnea detection are based on feature extraction from single-lead ECG signals and using classifiers [8][9][10][11]. Zarei and Mohammadzadeh Asl [10] proposed a novel approach based on single-lead ECG autoregressive (AR) modeling and ECG feature extraction using the spectral autocorrelation function.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

Faal

Almasganj

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.

show abstract

A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification

Chen

Lin

Chen

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.

show abstract

Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN‐LSTM Model

Cited by 28 publications

References 43 publications

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification

Contact Info

Product

Resources

About