Convolutional self-attention with adaptive channel-attention network for obstructive sleep apnea detection using limited training data

Obstructive sleep apnea (OSA) is a chronic sleep disorder caused by blockage of the upper airway for at least 10 s due to the collapsing of the tongue and soft palate. OSA can cause serious health problems including hypertension and coronary heart. Polysomnography is a technique to simultaneously record physiological signals such as electroencephalograms, electrooculograms, electrocardiograms (ECGs) etc., to diagnose various diseases including OSA. However, the process is time-consuming and tedious. Therefore, detecting OSA from ECGs (electrical signals recording heart variability using electrodes) is an alternative that can be extended to wearable devices. However, two challenges hinder their real-world applications: 1) Performance is directly proportional to the data size, and 2) algorithms are not robust for cross-dataset evaluation. We propose a novel deep-learning model called convolutional self-attention with adaptive channel-attention network (CSAC-Net) to address these issues. Specifically, the first issue is addressed by using the proposed Convolutional self-attention module in a multi-scale projection approach and fusing the features at the end. This enables the exploitation of long-range dependencies with diverse feature vectors. The second issue is addressed by leveraging invariant mapping through the proposed adaptive channel-attention (ACA) and inter-feature attention (IFA) modules. ACA module fuses multi-level features to embed adaptive characteristics while the IFA module exploits features from different stage to preserve the originality of features. To the best of our knowledge, this is the first study to address the underlying issues. Extensive experiments validate the effectiveness of CSAC-Net using two open databases: physiologic signal network apnea electrocardiogram (PhysioNet Apnea-ECG) and national sleep research resource best apnea interventions in research (NSRR-BestAIR). Their respective accuracies are respectively 93.4 % and 76.1 %, outperforming the state-of-the-art methods. Furthermore, the robustness of the CSAC-Net is validated through cross-database evaluation using various open databases.