Abstract
Obstructive sleep apnea (OSA) is one of the most common sleep-related breathing disorders, which causes various diseases and reduces life quality severely. In this paper, we propose OSA-Weigher, an automated computational framework that can improve the performance of identifying OSA events. Particularly, the key idea of OSA-Weigher is to subdivide each potential event segment (PES, i.e., a data segment that may or may not contain an OSA event) and to explore more information of respiratory pattern, so as to improve OSA events identification performance. Concretely, we utilize a micro-movement sensitive mattress (MSM) to get ballistocardiography (BCG) signal during sleep, and locate PESs by identifying the occurrence of arousals (i.e., a mechanism that makes patients recover from being apneic). Afterwards, we divide each PES into three phases (i.e., Apnea Phase, Respiratory Effort Phase and Arousal Phase) using a sliding window-based adaptive method. Based on these phases, we further extract and select efficient fine-grained features to characterize respiratory pattern from multiple aspects. Finally, these PESs are classified into OSA events or non-OSA events by employing an optimized ensemble classifier. Experimental results based on a real BCG dataset of 116 subjects show that OSA-Weigher outperforms the baseline method by 12.7% in terms of Precision, 14.8% in terms of Recall and 0.152 in terms of AUC (area under ROC curve).
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Source_1 means that features are directly extracted from whole PES segment, while Source_2 means that features are extracted from PES’s three different phases
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (No. 61332013), the National Key Research and Development Program of China (No. 2016YFB1001400), and the China Scholarship Council (No. 201706290110).
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Liu, F., Zhou, X., Wang, Z. et al. OSA-weigher: an automated computational framework for identifying obstructive sleep apnea based on event phase segmentation. J Ambient Intell Human Comput 10, 1937–1954 (2019). https://doi.org/10.1007/s12652-018-0787-2
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DOI: https://doi.org/10.1007/s12652-018-0787-2