2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDBIO-11.6
Paper Title LENGTH NO LONGER MATTERS: A REAL LENGTH ADAPTIVE ARRHYTHMIA CLASSIFICATION MODEL WITH MULTI-SCALE CONVOLUTION
Authors Chuanqi Han, Fang Yu, Peng Wang, Ruoran Huang, Xi Huang, Li Cui, Institute of Computing Technology, Chinese Academy of Sciences, China
SessionBIO-11: Deep Learning for Physiological Signals
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Although lots of arrhythmia classification models based on deep neural networks have been proposed, most of them can only be directly applied to inputs of a fixed length, which means the raw ECG records need to be padded or truncated before being put into the model. However, this process brings two main drawbacks: truncation may lead to information loss while padding increases calculation load. Besides, different sampling rates in other datasets may add trouble to transfer learning in this case. To address these problems, we propose a length adaptive arrhythmia classification model that can take advantage of raw ECG records of variable length. To further improve the overall performance, this model is designed with multi-scale convolution networks. We then introduce the dilated convolution that enables small convolution kernels to replace bigger traditional ones so as to enlarge feature's reception fields and reduce training parameters. Finally, an attention mechanism is implemented so that the model can get efficiently trained and output enhanced results. Extensive experiments on the benchmark MIT-BIH database prove that our method is competitive with other state-of-the-arts.