TY - GEN
T1 - A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
AU - Fotoohinasab, Atiyeh
AU - Hocking, Toby
AU - Afghah, Fatemeh
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - Changepoint detection
KW - Constraint learning
KW - ECG fiducial points detection
KW - Graph learning
UR - http://www.scopus.com/inward/record.url?scp=85107740815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107740815&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF51394.2020.9443307
DO - 10.1109/IEEECONF51394.2020.9443307
M3 - Conference contribution
AN - SCOPUS:85107740815
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 950
EP - 954
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Y2 - 1 November 2020 through 5 November 2020
ER -