An advanced two-step DNN-based framework for arrhythmia detection

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

14 Citations (Scopus)


Heart arrhythmia is a severe heart problem. Automated heartbeat classification provides a cost-effective screening for heart arrhythmia and allows at-risk patients to receive timely treatments, which is a highly demanded but challenging task. Recent works have brought visible improvements to this area, but to identify the problematic supraventricular ectopic (S-type) heartbeats is still a bottleneck in most existing studies. This paper presents a two-step DNN-based framework to identify arrhythmia-related heartbeats. In the first step, a deep dual-channel convolutional neural network (DDCNN) is proposed to classify all heartbeat classes, except for the normal and S-type heartbeats. In the second stage, a central-towards LSTM supportive model (CLSM) is specially designed to distinguish S-type heartbeats from the normal ones. By processing heart rhythms in central-towards directions, CLSM learns and abstracts hidden temporal information between a heartbeat and its neighbors to reveal the deep differences between the two heartbeat types. As an improvement, we also propose a rule-based data augmentation method to solve the training data imbalance problem. The proposed framework is evaluated over three real-world ECG databases. The results show that our method outperforms the baselines in most evaluation metrics.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication24th Pacific-Asia Conference, PAKDD 2020 Singapore, May 11–14, 2020 Proceedings, Part II
EditorsHady W. Lauw, Raymond Chi-Wing Wong, Alexandros Ntoulas, Ee-Peng Lim, See-Kiong Ng, Sinno Jialin Pan
Place of PublicationCham Switzerland
Number of pages13
ISBN (Electronic)9783030474362
ISBN (Print)9783030474355
Publication statusPublished - 2020
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020
Conference number: 24th (Website) (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2020
Abbreviated titlePAKDD 2020
Internet address


  • Arrhythmia detection
  • Data augmentation
  • Deep learning

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