Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network

Abstract

Sleep apnea (SA) is a ubiquitous sleep-related respiratory disease. It can occur hundreds of times at night, and its long-term occurrences can lead to some serious cardiovascular and neurological diseases. Polysomnography (PSG) is a commonly used diagnostic device for SA. But it requires suspected patients to sleep in the lab for one to two nights and records about 16 signals through expert monitoring. The complex processes hinder the widespread implementation of PSG in public health applications. Recently, some researchers have proposed using a single-lead ECG signal for SA detection. These methods are based on the hypothesis that the SA relies only on the current ECG signal segment. However, SA has time dependence; that is, the SA of the ECG segment at the previous moment has an impact on the current SA diagnosis. In this study, we develop a time window artificial neural network that can take advantage of the time dependence between ECG signal segments and does not require any prior assumptions about the distribution of training data. By verifying on a real ECG signal dataset, the performance of our method has been significantly improved compared to traditional non-time window machine learning methods as well as previous works.

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network

“…In particular, we can compare our method with that of Tao Wang et al . [ 62 ]. Their method obtained an overall accuracy, sensitivity, and specificity of 87.3%, 85.1%, and 88.7%, respectively for the withheld dataset, whereas our method achieved an average accuracy, sensitivity, and specificity of 92.4%, 92.3%, and 92.6%, respectively, representing a significant performance improvement with the same dataset.…”

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

“…Wang et al [ 42 ] use ECG signals for sleep apnea detection. R-R intervals and R-peak amplitudes were extracted from ECG signals, and time window ANN was applied for classification.…”

“… Accuracy: 99.0% [ 40 ] 2018 Apnea-ECG @ ECG DNN, HMM SVM, ANN Sparse auto-encoders was used to learn features via unsupervised learning. Accuracy: 85%; [ 42 ] 2019 Apnea-ECG @ ECG Time window ANN R-R intervals and R-peak amplitudes were extracted from ECG signals. Further, 6 time domain and 6 frequency domain features from R-R interval, and 6 frequency domain features from R-peak amplitudes were extracted.…”

Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

“…Several separated OSA detection methods have been proposed up until now. [ 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ] Most of these methods have consisted of feature extraction, feature selection, and classifier parts. In Figure 1 , we can see the collective flowchart of an OSA detection approach based on the ECG signal processing:…”

“…[ 5 ] have proposed the usage of the support vector machines (SVMs) for classifying between the apnea and normal ECG signal. Other classifiers like the Neural networks (NNs) have been proposed by Khandoker, et al .,[ 3 ] the Random forest by[ 7 ] and[ 8 ] the Adaboost classifier by,[ 11 ] Rusboost by,[ 12 ] Boot-strap by,[ 13 ] Convolutional NNs (CNNs) by[ 14 15 16 17 ] and Deep NNs (DNNs) by[ 18 ] for OSA detection. However, the SVMs are still the most prevalently used classifiers in this topic and we compared our results to these classifiers.…”