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Conference Paper
Arrhythmia Detection Using Convolutional Neural Networks with Temporal Attention Mechanism
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- Authors
- Muhammad Zubair, Sungpil Woo, Sunhwan Lim, Chan-Won Park
- Issue Date
- 2021-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.1101-1103
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper presents a deep learning approach for arrhythmia detection using convolutional neural networks with attention mechanism. In order to learn an efficient deep feature representation from raw ECG data, we proposed a temporal attention module that extract target-specific information. The proposed two-branch attention module aims to extract short-term and long-term morphological patterns of the beats with different convolutional kernels. We also followed the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for beat labeling. The experimental results reveal that the proposed approach significantly discriminate between different beats types without using hand crafted features and achieve an improved classification performance as compared to other methods.
- KSP Keywords
- Attention mechanism, Classification Performance, Convolution neural network(CNN), Learning approach, Medical instrumentation, Morphological patterns, arrhythmia detection, deep feature representation, deep learning(DL), neural network(NN), short-term