Identifying Obstructive Sleep Apnea by Exploiting Fine-Grained BCG Features Based on Event Phase Segmentation

Obstructive sleep apnea (OSA) is regarded as one of the most common sleep-related breathing disorders, which causes various diseases and affects people's daily life severely. Up to now, massive efforts have been devoted to identifying OSA events during sleep based on different signals (e.g., PSG, ECG, nasal airflow and EMG, etc.). However, there still are more or less shortcomings in current studies. In this paper, we propose a novel framework to improve the performance of identifying OSA events. Particularly, the key idea of our framework is to divide each potential event segment (i.e., a data segment that may or may not contain an OSA event) into different phases, from which we further extract fine-grained features to characterize respiratory pattern comprehensively. Concretely, we first automatically locate potential event segments from raw ballistocardiography (BCG) data by identifying arousals. Afterwards, each potential event segment is divided into three phases (i.e., Apnea Phase, Respiratory Effort Phase and Arousal Phase) by an adaptive threshold-based division algorithm. Based on these phases, we further extract and select efficient features that can characterize respiratory pattern from different aspects. Finally, these potential event segments are classified into OSA events or non-OSA events using BP neural network. Experimental results based on a real BCG dataset that contains 3,790 OSA events and 2,556 non-OSA events show that our framework outperforms the baselines and the precision, recall and AUC reach 94.6%, 93.1%, and 0.951, respectively.