Accurate ECG Classification Based on Spiking Neural Network and Attentional Mechanism for Real-Time Implementation on Personal Portable Devices

Yuxuan Xing, Lei Zhang, Zhixian Hou, Xiaoran Li*, Yueting Shi, Yiyang Yuan, Feng Zhang, Sen Liang, Zhenzhong Li, Liang Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Electrocardiogram (ECG) heartbeat classification plays a vital role in early diagnosis and effective treatment, which provide opportunities for earlier prevention and intervention. In an effort to continuously monitor and detect abnormalities in patients’ ECG signals on portable devices, this paper present a lightweight ECG heartbeat classification method based on a spiking neural network (SNN), a relatively shallow SNN model integrated with a channel-wise attentional module. We further explore the best-optimized architecture, which benefits from leveraging the full advantages of the SNN potential with the attention mechanism to process the classification task at low power and capture prominent features concerning the time, morphology, and multi-channel representations of the ECG signal. Results show that our model achieves overall classification accuracy of 98.26%, sensitivity of 94.75%, and F1 score of 89.09% on the MIT-BIH database, with energy consumption of 346.33 µJ per beat and runtime of 1.37 ms. Moreover, we have conducted multiple experiments to compare against current state-of-the-art methods using their assessment strategies to evaluate our model implementation on FPGA. So far, our work achieves comparable overall performance with all the literature in terms of classification accuracy, energy consumption, and real-time capability.

Original languageEnglish
Article number1889
JournalElectronics (Switzerland)
Volume11
Issue number12
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • MIT-BIH database
  • channel attentional mechanism
  • electrocardiogram (ECG) classification
  • portable devices
  • spiking neural network (SNN)

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Xing, Y., Zhang, L., Hou, Z., Li, X., Shi, Y., Yuan, Y., Zhang, F., Liang, S., Li, Z., & Yan, L. (2022). Accurate ECG Classification Based on Spiking Neural Network and Attentional Mechanism for Real-Time Implementation on Personal Portable Devices. Electronics (Switzerland), 11(12), Article 1889. https://doi.org/10.3390/electronics11121889