Abstract
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 language | English |
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Title of host publication | Advances in Knowledge Discovery and Data Mining |
Subtitle of host publication | 24th Pacific-Asia Conference, PAKDD 2020 Singapore, May 11–14, 2020 Proceedings, Part II |
Editors | Hady W. Lauw, Raymond Chi-Wing Wong, Alexandros Ntoulas, Ee-Peng Lim, See-Kiong Ng, Sinno Jialin Pan |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 422-434 |
Number of pages | 13 |
ISBN (Electronic) | 9783030474362 |
ISBN (Print) | 9783030474355 |
DOIs | |
Publication status | Published - 2020 |
Event | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2020 - Singapore, Singapore Duration: 11 May 2020 → 14 May 2020 Conference number: 24th https://pakdd2020.org (Website) https://link.springer.com/book/10.1007/978-3-030-47426-3 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12085 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2020 |
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Abbreviated title | PAKDD 2020 |
Country/Territory | Singapore |
City | Singapore |
Period | 11/05/20 → 14/05/20 |
Internet address |
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Keywords
- Arrhythmia detection
- Data augmentation
- Deep learning