System Light-Loading Technology for mHealth: Manifold-Learning-Based   Medical Data Cleansing and Clinical Trials in WE-CARE Project

doi:10.1109/JBHI.2013.2292576

CORC > 北京大学 > 信息科学技术学院

	System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing and Clinical Trials in WE-CARE Project
	Huang, Anpeng ; Xu, Wenyao ; Li, Zhinan ; Xie, Linzhen ; Sarrafzadeh, Majid ; Li, Xiaoming ; Cong, Jason
刊名	ieee journal of biomedical and health informatics
	2014
关键词	Manifold learning Manifold-based ECG-feature Purification (MEP) mHealth (mobile health) system light-loading Wearable Efficient teleCARdiology systEm (WE-CARE) WAVE
DOI	10.1109/JBHI.2013.2292576
英文摘要	Cardiovascular disease (CVD) is a major issue to public health. It contributes 41% to the Chinese death rate each year. This huge loss encouraged us to develop a Wearable Efficient tele-CARdiology systEm (WE-CARE) for early warning and prevention of CVD risks in real time. WE-CARE is expected to work 24/7 online for mobile health (mHealth) applications. Unfortunately, this purpose is often disrupted in system experiments and clinical trials, even if related enabling technologies work properly. This phenomenon is rooted in the overload issue of complex Electrocardiogram (ECG) data in terms of system integration. In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate. To achieve this objective, we propose an approach to purify clinical features from ECG raw data based on manifold learning, called the Manifold-based ECG-feature Purification algorithm. Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria. Most importantly, the experiment results demonstrate that the WE-CARE system enabled by our proposal can enhance system reliability by at least two times and reduce false negative rates to 0.76%, and extend the battery life by 40.54%, in the system integration level.; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics; SCI(E); 1; ARTICLE; hapku@pku.edu.cn; wenyaoxu@buffalo.edu; lizhinan@pku.edu.cn; tydxlz@pku.edu.cn; majid@cs.ucla.edu; lxm@pku.edu.cn; cong@cs.ucla.edu; 5; 1581-1589; 18
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/152011]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Huang, Anpeng,Xu, Wenyao,Li, Zhinan,et al. System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing and Clinical Trials in WE-CARE Project[J]. ieee journal of biomedical and health informatics,2014.
APA	Huang, Anpeng.,Xu, Wenyao.,Li, Zhinan.,Xie, Linzhen.,Sarrafzadeh, Majid.,...&Cong, Jason.(2014).System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing and Clinical Trials in WE-CARE Project.ieee journal of biomedical and health informatics.
MLA	Huang, Anpeng,et al."System Light-Loading Technology for mHealth: Manifold-Learning-Based Medical Data Cleansing and Clinical Trials in WE-CARE Project".ieee journal of biomedical and health informatics (2014).