Name
Title | ||
|---|---|---|
Hardware-assisted and Deep-Learning techniques for Low-Power Detection of Cardiovascular Abnormalities in Smart Wearables |
Abstract | ||
|---|---|---|
The COVID-19 pandemic has significantly reduced visits to hospitals and clinics, forcing physicians and clinics to investigate how to move online using telemedicine and home monitoring. Wearable technologies can help by enabling homecare monitoring if they provide accurate and precise measurements. The monitoring of cardiac health problems is such an example and can be managed when patients are re... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SmartIoT52359.2021.00031 | 2021 IEEE International Conference on Smart Internet of Things (SmartIoT) |
Keywords | DocType | ISBN |
Cardiac Arrhythmias,ECG signal classification,Deep learning,Convolutional neural networks,Wearable Electronics,Experimental Evaluation | Conference | 978-1-6654-4511-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Catalani | 1 | 0 | 0.34 |
| Ioannis Chatzigiannakis | 2 | 1238 | 121.01 |
| Aris Anagnostopoulos | 3 | 1054 | 67.08 |
| G. Akrivopoulou | 4 | 0 | 0.34 |
| D. Amaxilatis | 5 | 0 | 0.34 |
| A. Antoniou | 6 | 0 | 0.34 |