System light-loading technology for mHealth: Manifold-learning-based medical data cleansing and clinical trials in WE-CARE Project.
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Date
2014-09-06
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Abstract
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 teleCARdiology 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