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Title: A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to other state-of-the-art approaches. The proposed method achieves the overall sensitivity of Sen = 99.64%, positive predictivity of PPR = 99.71%, and detection error rate of DER = 0.19.  more » « less
Award ID(s):
1657260
PAR ID:
10233269
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE Asilomar Conference on Signals, Systems, and Computers ASILOMAR
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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