Towards End-to-End ECG Classification with Raw Signal Extraction and Deep Neural Networks

Sean Shensheng Xu, Man Wai Mak, Chi Chung Cheung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

54 Citations (Scopus)

Abstract

This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals into heartbeat types, i.e., normal beat or different types of arrhythmias. Time-domain sample points are extracted from raw ECG signals, and consecutive vectors are extracted from a sliding time-window covering these sample points. Each of these vectors comprises the consecutive sample points of a complete heartbeat cycle, which includes not only the QRS complex but also the P and T waves. Unlike existing heartbeat classification methods in which medical doctors extract handcrafted features from raw ECG signals, the proposed end-to-end method leverages a deep neural network for both feature extraction and classification based on aligned heartbeats. This strategy not only obviates the need to handcraft the features but also produces optimized ECG representation for heartbeat classification. Evaluations on the MIT-BIH arrhythmia database show that at the same specificity, the proposed patient-independent classifier can detect supraventricular- and ventricular-ectopic beats at a sensitivity that is at least 10% higher than current state-of-the-art methods. More importantly, there is a wide range of operating points in which both the sensitivity and specificity of the proposed classifier are higher than those achieved by state-of-the-art classifiers. The proposed classifier can also perform comparable to patient-specific classifiers, but at the same time enjoys the advantage of patient independence.

Original languageEnglish
Article number8468972
Pages (from-to)1574-1584
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number4
DOIs
Publication statusPublished - Jul 2019

Keywords

  • arrhythmia classification
  • deep neural networks
  • ECG classification
  • end-to-end
  • heartbeat alignment

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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