A pyramid-like model for heartbeat classification from ECG recordings

Jinyuan He, Le Sun, Jia Rong, Hua Wang and Yanchun Zhang

PLOS ONE, 2018, vol. 13, issue 11, 1-19

Abstract: Heartbeat classification is an important step in the early-stage detection of cardiac arrhythmia, which has been identified as a type of cardiovascular diseases (CVDs) affecting millions of people around the world. The current progress on heartbeat classification from ECG recordings is facing a challenge to achieve high classification sensitivity on disease heartbeats with a satisfied overall accuracy. Most of the work take individual heartbeats as independent data samples in processing. Furthermore, the use of a static feature set for classification of all types of heartbeats often causes distractions when identifying supraventricular (S) ectopic beats. In this work, a pyramid-like model is proposed to improve the performance of heartbeat classification. The model distinguishes the classification of normal and S beats and takes advantage of the neighbor-related information to assist identification of S bests. The proposed model was evaluated on the benchmark MIT-BIH-AR database and the St. Petersburg Institute of Cardiological Technics(INCART) database for generalization performance measurement. The results reported prove that the proposed pyramid-like model exhibits higher performance than the state-of-the-art rivals in the identification of disease heartbeats as well as maintains a reasonable overall classification accuracy.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0206593

DOI: 10.1371/journal.pone.0206593

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