A novel deep domain adaptation for automated detection of sleep apnea/hypopnea events


 Abstract Objective: Sleep apnea-hypopnea syndrome (SAHS) is a common sleep-related respiratory disorder that is generally assessed for severity using polysomnography (PSG), but the diversity of sampling devices and patients makes it not only costly, but the performance of the algorithms may also be degraded by this diversity. Approach: This paper proposed a novel deep domain adaptation module, which used an LSTM-CNN network embedded with the channel attention mechanism to achieve autonomous extraction of high-quality features. Meanwhile, a domain adaptation module was built to achieve domain invariant feature extraction for reducing the differences in data distribution caused by different devices and other factors. In addition, during the training process, the algorithm used the last second label as the label of the PSG segment, so that the second-by-second evaluation of respiratory events could be achieved. Main results: The algorithm applied the two datasets provided by PhysioNet as the source and target domains. The accuracy, sensitivity and specificity of the algorithm on the source domain were 86.46%, 86.11% and 93.17%, respectively, and on the target domain were 83.63%, 82.52%, 91.62%, respectively. The proposed algorithm showed strong generalization ability and the classification results were comparable to the current advanced methods. Besides, the apnea-hypopnea index (AHI) values estimated by the proposed algorithm showed a high correlation with the manual scoring values on both domains. Significance: The proposed algorithm can effectively perform SAHS detection and evaluation with certain generalization.