Name
Abstract | ||
|---|---|---|
Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TBCAS.2019.2930215 | IEEE transactions on biomedical circuits and systems |
Keywords | Field | DocType |
Electrocardiography,Heart beat,Biomedical monitoring,Power demand,Feature extraction,Monitoring,Neural networks | Wearable computer,Efficient energy use,Computer science,Heart problem,Real-time computing,Feature extraction,Electronic engineering,Medical treatment,Wearable technology,Artificial neural network,Power consumption | Journal |
Volume | Issue | ISSN |
13 | 5 | 1932-4545 |
Citations | PageRank | References |
7 | 0.49 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Wang | 1 | 230 | 87.46 |
| Jun Zhou | 2 | 23 | 6.15 |
| Guanghai Dai | 3 | 19 | 1.38 |
| Jiahui Huang | 4 | 12 | 1.26 |
| Yuxiang Xie | 5 | 24 | 2.94 |