An one-dimensional signal based object detection network for apnea and hypopnea locating

Abstract

Sleep-disordered breathing (SDB), a common sleep disorder, shows symptoms of shallow breathing or paused breathing during sleep called respiratory events. SDB was conventionally diagnosed based on overnight multi-channel polysomnography (PSG) in clinical treatment. However, this process requires experienced sleep technicians to annotate and is quite labour-intensive. In this study, a novel one-dimensional signal based object detection network was proposed for automatic, high efficiency detection and classification of different kinds of respiratory events from continuous PSG signals. Our method can locate respiratory events in PSG signal data and classify them into four categories for further clinical treatment. The method was further validated on a PSG clinical dataset collected from Beijing Tongren Hospital. Precision, recall and F1-score of 84.9%, 85.1%, 85.0% were achieved for events detection with total accuracy rate reaching 74.9% in classification of detected events. The result shows that one-dimensional signal object detection is a promising method to locate the characteristic waveform and extract signal features. Such method can be applied in other signal feature detection field.