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COMPRESSIVE SENSING BASED ECG MONITORING WITH EFFECTIVE AF DETECTION

Citation Author(s):
Hung-Chi Kuo, Yu-Min Lin and An-Yeu (Andy) Wu
Submitted by:
Hung Chi Kuo
Last updated:
1 March 2017 - 1:50am
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Hung-Chi Kuo
Paper Code:
BISP-P3.7
 

Atrial fibrillation (AF) patients need long-term electrocardiography (ECG) monitoring to detect occurrence of AF. We can acquire ECG signals under low power by compressive sensing based sensor and detect AF by existing algorithms. However, the compression ratio of AF signal is low when DWT basis is applied for CS reconstruction. On the other hand the complexity of AF detection algorithms is high. In this paper, we propose a CS-based ECG monitoring system with effective AF detection. We exploit dictionary learning to improve 2.5x better compression ratio than existing works. With built-in AF detection, we can detect AF with 96.0% sensitivity and 97.2% specificity from highly compressed data, without any complex detection algorithm.

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