Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram

•Deep learning approaches were designed to automatically detect sleep apnea (SA) from an electrocardiogram signal.•Six deep learning approaches were designed and implemented including DNN, 1D CNN, 2D CNN, RNN, long short-term memory, and gated-recurrent unit models.•The 1D CNN and GRU models were the best-performing of the accuracy was 99.0% and recall was 99.0%.•The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events. This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal. Six deep learning approaches were designed and implemented for automatic detection of SA events including deep neural network (DNN), one-dimensional (1D) convolutional neural networks (CNN), two-dimensional (2D) CNN, recurrent neural networks (RNN), long short-term memory, and gated-recurrent unit (GRU). Designed deep learning models were analyzed and compared in the performances. The ECG signal was pre-processed, normalized, and segmented into 10 s intervals. Subsequently, the signal was converted into a 2D form for analysis in the 2D CNN model. A dataset collected from 86 patients with SA was used. The training set comprised data from 69 of the patients, while the test set contained data from the remaining 17 patients. The accuracy of the best-performing model was 99.0%, and the 1D CNN and GRU models had 99.0% recall rates. The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events, and they could distinguish between apnea and hypopnea events using an ECG signal. The deep learning approaches such as 1D CNN and GRU can be helpful tools to automatically detect SA in sleep apnea screening and related studies.