Abstract
This study investigates ECG features, focusing on T-wave amplitude, from a wearable ECG device as a potential method for fitness monitoring in exercise rehabilitation. An automatic T-peak detection algorithm is presented that uses local baseline detection to overcome baseline drift without the need for preprocessing, and offers adequate performance on data recorded in noisy environments. The algorithm is applied to 24 hour data recordings from two subject groups with different physical activity histories. Results indicate that, while mean heart rate (HR) differs most significantly between the groups, T-amplitude features could be useful depending on the disparities in fitness level, and require further investigation on an individual basis.
Original language | English |
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Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Publisher | IEEE |
Publication date | 2014 |
Pages | 4172-4175 |
ISBN (Print) | 978-1-4244-7929-0 |
DOIs | |
Publication status | Published - 2014 |
Event | 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States Duration: 26 Aug 2014 → 30 Aug 2014 Conference number: 36 http://embc.embs.org/2014/ |
Conference
Conference | 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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Number | 36 |
Country/Territory | United States |
City | Chicago, IL |
Period | 26/08/2014 → 30/08/2014 |
Internet address |
Keywords
- Bioengineering
- Atmospheric measurements
- Electrocardiography
- Feature extraction
- Heart rate
- Particle measurements
- Training
- Transforms