Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

Bach Tung Pham, Phuong Thi Le, Tzu Chiang Tai, Yi Chiung Hsu, Yung-Hui Li, Jia Ching Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

Original languageEnglish
Article number2993
JournalSensors (Switzerland)
Volume23
Issue number6
DOIs
StatePublished - Mar 2023

Keywords

  • MIT-BIH dataset
  • PTB dataset
  • deep learning
  • electrocardiogram (ECG) classification

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