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

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.

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

“…A few articles adopted a case-control [19,41,42,50,54] or randomized control research design [16,28,56]. For some articles [11,18,21,23,24,58], information provided on how the experiments were conducted was not sufficient for determining the research design adopted.…”

Interactive crowdsourcing to spontaneous reporting of Adverse Drug Reactions

“…Experiments were carried out and the results show that our system could effectively generate an informative report upon consumers' reporting of an adverse drug event. This system is part of the follow-up work of WE-CARE [21], [22]. Our further research will focus on the application of DMAs in the signal detection module as well as the techniques for denoising the signal.…”

Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

“…The works reported upon in this section include 24/7 systems for ECG monitoring [18][19][20], a preliminary work on person identification aiming toward zero-effort monitoring of cardiovascular diseases [21], three works aiming toward estimating BP continuously without wearing a BP cuff [22][23][24] and work on a bipolar wireless ECG monitor [25,26]. The device was later certified as a medical device, and Reference [27] reported on a number of studies conducted with the device.…”