Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

Abstract

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram

“…These studies all adopted the MIT PhysioNet apnea-ECG database, and the released and withheld datasets were used for training and validation, respectively. Our study and the studies of Singh and Majumder [15], Wang et al [16], Figure 7. Curves of the number of feature extraction layers vs.…”

“…These studies all adopted the MIT PhysioNet apnea-ECG database, and the released and withheld datasets were used for training and validation, respectively. Our study and the studies of Singh and Majumder [15], Wang et al [16], and Li et al [17] proposed feature-learning-based methods which can automatically learn the features of ECG signals or RR intervals using neural networks. The studies of Sharma and Sharma [12], Song et al [13], and Varon et al [14] focused on feature-engineering-based methods.…”

“…The other studies all required R-peaks detection and RR interval calculation. The media filtering [13,16,17] and R-peaks correction [14] have often been used to eliminate the physiologically uninterpretable points. Interpolation [16,17] has been used to interpolate all RR interval series to have the same length.…”

“…Singh and Majumder [15] proposed a pre-trained two-dimensional (2D) AlexNet model based on a convolutional neural network (CNN) to extract the features from 2D time-frequency images of ECG signals, and a decision fusion method consisting of the KNN, SVM, LDA, and Ensemble classifiers to improve the sensitivity for detecting apnea events. Wang et al [16] proposed a modified LeNet-5 CNN model to extract features from 1D ECG signals and RR intervals, and to classify the normal and apnea events. Li et al [17] developed a deep neural network to learn features from RR intervals, which belongs to unsupervised learning that only requires unlabeled ECG signals, and designed two types of classifiers including SVM and artificial neural network (ANN) to detect the apnea events.…”

Deep Learning in Physiological Signal Data: A Survey

“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”