@inproceedings{13a65570543d4291903c7935c943ed00,
title = "Poster abstract: Breathing disorder detection using wearable electrocardiogram and oxygen saturation",
abstract = " Conventional diagnosis using polysomnography (PSG) on breathing disorder is expensive and uncomfortable to patients. In this paper, we present a low-cost portable and wearable multi-sensor system to non-invasively acquire a subject{\textquoteright}s vital signs, and leverage various machine learning methods on features extracted from Electrocardiogram (ECG) and Blood oxygen saturation (SpO 2 ) signals to detect breathing disorder events. Our preliminary predication accuracies on 110 clinical patients is 90.0\%.",
keywords = "Breathing disorder, Healthcare, Machine learning, Wearable sensors",
author = "Yuezhou Zhang and Zhicheng Yang and Zhengbo Zhang and Peiyao Li and Desen Cao and Xiaoli Liu and Jiewen Zheng and Qian Yuan and Jianli Pan",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018 ; Conference date: 04-11-2018 Through 07-11-2018",
year = "2018",
month = nov,
day = "4",
doi = "10.1145/3274783.3275159",
language = "英语",
series = "SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "313--314",
booktitle = "SenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems",
}