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Personalized Arrhythmia Detection Based on Lightweight Autoencoder and Variational Autoencoder

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Databases Theory and Applications (ADC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13459))

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Abstract

Arrhythmia has become one of the important causes of human death. The research on arrhythmia detection has great medical value. In reality, patients’ arrhythmia heartbeat is much less than the normal heartbeat. Supervised classifiers often have the problem of imbalanced training data. Therefore, we propose an unsupervised personalized arrhythmia detection system, called PerAD. PerAD trains a lightweight autoencoder ShaAE for each user for arrhythmia detection. ShaAE only needs to use the user’s personal normal data for training. The encoder and decoder of ShaAE are composed of a lightweight network ShaRNN. ShaRNN is a two-layer RNN structure that can process data in parallel. Thus, ShaAE is easy to deploy to edge wearable devices. We also design a fast-inference variational autoencoder to generate normal simulation samples to assist in training ShaAE. We test ShaAE on MIT-BIH Arrhythmia Database. ShaAE without using simulation data to assist training can achieve 96.86\(\%\) accuracy. ShaAE using simulation samples to assist training can achieve accuracy of 97.11\(\%\) and has 6.19\(\%\) higher performance than state-of-the-art for f1 score.

This work is partially supported by the National Natural Science Foundation of China (Grants No. 61702274), PAPD and the Major Key Project of PCL (Grant No. PCL2022A03, PCL2021A02, PCL2021A09).

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Authors and Affiliations

  1. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoyi Zhong & Le Sun

  2. Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhaoyi Zhong & Le Sun

  3. Victoria University, Footscray, Australia

    Sudha Subramani

Authors
  1. Zhaoyi Zhong
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  2. Le Sun
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  3. Sudha Subramani
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Correspondence to Le Sun .

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Editors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Wen Hua

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China

    Lei Li

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Zhong, Z., Sun, L., Subramani, S. (2022). Personalized Arrhythmia Detection Based on Lightweight Autoencoder and Variational Autoencoder. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-15512-3_4

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  • Online ISBN: 978-3-031-15512-3

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