ECG P Wave Detection Combining Particle Swarms and Energy Difference Between Bilateral Long and Short Windows

doi:10.12068/j.issn.1005-3026.2022.04.005

Abstract

Abstract: The ECG P wave contains abundant physiological and pathological information of the human body， but the P wave amplitude is small and the shape is changeable， which is very difficult to detect. This paper proposes a bilateral long-short-window energy difference method to detect multi-modal P wave boundaries， and uses the particle swarm algorithm to optimize its parameters. 4363 and 1936 ECG beats are selected from the LUE database and QT database respectively， with 70% and 30% of them used as the training set and the test set， respectively. Compared with the parameter-mixed Gaussian fitting method based on dynamic programming and the phase-free stationary wavelet transform method， the error of the results of positive and negative P waves is significantly smaller than the above two methods， and the algorithm can detect bidirectional P waves， and it also has anti-noise ability.

Key words: ECG signal; P wave detection; polymorphic ECG P wave; bilateral long-short-window energy difference method; particle swarm

CLC Number:

TP391.4

XU Li-sheng， SU Yu-jian， TAN Jun-yi， FANG Xi-dong. ECG P Wave Detection Combining Particle Swarms and Energy Difference Between Bilateral Long and Short Windows[J]. Journal of Northeastern University(Natural Science), 2022, 43(4): 488-495.

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