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).
Obstructive sleep apnea OSA Apnea Respiratory effort Arousal Event structure
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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).
Daulatzai MAKA., Khandoker AH, Karmakar CK et al (2009) Characterization of chimeric surface submentalis EMG activity during hypopneas in obstructive sleep apnea patients. In: Science and Technology for Humanity (TIC-STH), Toronto International Conference, IEEE, pp 782–788. https://doi.org/10.1109/tic-sth.2009.5444394
Isa SM, Fanany MI, Jatmiko W et al (2011) Sleep Apnea Detection from ECG signal: analysis on optimal features, principal components, and nonlinearity. In: Bioinformatics and Biomedical Engineering (iCBBE), IEEE 5th International Conference on, pp 1–4. https://doi.org/10.1109/icbbe.2011.5780285
Koley B, Dey D (2012b) Automated detection of apnea and hypopnea events. In: Emerging Applications of Information Technology (EAIT), IEEE 3rd International Conference on, pp 85–88. https://doi.org/10.1109/eait.2012.6407868
Liu F, Zhou X, Wang Z et al (2016a) A light-weight data preprocessing and integrative scheduling framework for health monitoring. In: Biomedical and Health Informatics (BHI), IEEE-EMBS International Conference on, pp 192–195. https://doi.org/10.1109/bhi.2016.7455867
Liu F, Zhou X, Wang Z et al (2016b) Identifying obstructive sleep apnea by exploiting fine-grained BCG features Based on Event Phase Segmentation. In: Bioinformatics and bioengineering (BIBE), IEEE 16th International Conference on, pp 293–300. https://doi.org/10.1109/bibe.2016.45
Nandakumar R, Gollakota S, Watson N (2015) Contactless sleep apnea detection on smartphones. In: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services. ACM, pp 45–57. https://doi.org/10.1145/2742647.2742674
Samy L, Macey PM, Alshurafa N et al (2015) An automated framework for predicting obstructive sleep apnea using a brief, daytime, non-intrusive test procedure. In: Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments, pp 70. https://doi.org/10.1145/2769493.2769541
Zhang J, Zhang Q, Wang Y et al (2013) A real-time auto-adjustable smart pillow system for sleep apnea detection and treatment. In: Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN), ACM, pp 179–190. https://doi.org/10.1145/2461381.2461405
Zhao W, Ni H, Zhou X et al (2015) Identifying sleep apnea syndrome using heart rate and breathing effort variation analysis based on ballistocardiography. In: Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE, IEEE, pp 4536–4539. https://doi.org/10.1109/embc.2015.7319403