Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JBHI.2013.2292576 | IEEE J. Biomedical and Health Informatics |
Keywords | Field | DocType |
clinical criteria,electrocardiography,information storage,biomedical telemetry,manifold learning-based medical data cleansing,clinical trial,early cvd risk warning,medical signal detection,diseases,telemedicine,clinical feature purification,wearable efficient telecardiology system,daily public health-risk alert applications,learning (artificial intelligence),alarm systems,complex electrocardiogram data,mhealth (mobile health),mhealth applications,medical signal processing,wearable efficient telecardiology system (we-care),cardiovascular disease,body sensor networks,real time cvd risk warning,ecg raw data,internet,feature extraction,risk management,chinese death rate,we-care system,signal classification,complex ecg data overload issue,system integration level,system reliability,false negative rate reduction,benchmarked ecg anomaly recognition rate,online system,system experiments,we-care project,mobile health applications,system light-loading technology,manifold-based ecg-feature purification algorithm,enabling technology,cvd prevention,mobile computing,anomay detection,biomedical electronics,battery life,manifold-based ecg-feature purification (mep),manifold learning,system light-loading | Warning system,Data mining,Data cleansing,Computer science,Raw data,Clinical trial,mHealth,Artificial intelligence,Nonlinear dimensionality reduction,System integration,Computer vision,Wearable computer,Machine learning | Journal |
Volume | Issue | ISSN |
18 | 5 | 2168-2208 |
Citations | PageRank | References |
9 | 0.72 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anpeng Huang | 1 | 151 | 21.31 |
| Wenyao Xu | 2 | 615 | 77.06 |
| Zhinan Li | 3 | 43 | 7.08 |
| Linzhen Xie | 4 | 61 | 10.32 |
| Majid Sarrafzadeh | 5 | 3103 | 317.63 |
| Xiaoming Li | 6 | 1669 | 92.16 |
| Jason Cong | 7 | 7069 | 515.06 |