Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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A Novel Spiking Neural Network for ECG signal Classification

  • Rana, Amrita (Department of Electronic Engineering, Daegu University) ;
  • Kim, Kyung Ki (Department of Electronic Engineering, Daegu University)
  • Received : 2021.01.21
  • Accepted : 2021.01.30
  • Published : 2021.01.31
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Abstract

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

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References

  1. E. J. Benjamin, S. S. Virani, C. W. Callaway, A.M. Chamberlain, A. R. Chang, S. Cheng, et al., Heart disease and stroke statistics-2018 update: A report from the American heart association. Circulation, Vol. 137, No. 12, pp. 67, Jan. 2018.
  2. R. Avanzato, F. Beritelli, "Automatic ECG Diagnosis Using Convolutional Neural Network", Electronics, Vol. 9, No. 6, pp. 1-14, Jun. 2020.
  3. P. Zhang, J. Cheng, Y. Zhao, "Classification of ECG Signals Based on LSTM and CNN", Artificial Intelligence and Security, 6th International Conference (ICAIS), pp. 278-289, Sep. 2020.
  4. S. Singh, S. K. Pandey, U. Pawar, R. R. Janghel, "Classification of ECG Arrhythmia using Recurrent Neural Networks", International Conference on Computational Intelligence and Data Science (ICCIDS ), pp. 1290-1297, May. 2018.
  5. S. Kiranyaz, T. Ince, and M. Gabbouj, "Real-time patient-specific ECG classification by 1-d convolutional neural networks," IEEE Trans. Biomed. Eng., Vol. 63, No. 3, pp. 664-675, Aug. 2016. https://doi.org/10.1109/TBME.2015.2468589
  6. Y. H. Hu, S. Palreddy, and W. J. Tompkins, "A patientadaptable ECG beat classifier using a mixture of experts approach," IEEE Trans. Biomed. Eng., Vol. 44, No. 9, pp. 891-900, 1997. https://doi.org/10.1109/10.623058
  7. T. Ince, S. Kiranyaz, and M. Gabbouj, "A generic and robust system for automated patient-specific classification of electrocardiogram signals," IEEE Trans. Biomed. Eng., Vol. 56, No. 5, pp. 1415-1426, May. 2009. https://doi.org/10.1109/TBME.2009.2013934
  8. M. R. Azghadi, N. Iannella, S. F. Al-Sarawi, G. Indiveri, et al., "Spike-based synaptic plasticity in silicon: Design, implementation, application, and challenges," Proc. IEEE, Vol. 102, No. 5, pp. 717-737, May 2014.
  9. N. Qiao and G. Indiveri, "Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies," Proceedings of IEEE Biomed. Circuits and Systems Conference (BioCAS), pp. 552-555, Aug. 2016.
  10. A. Amirshahi and M. Hashemi, "Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices", IEEE Trans Biomed Circuits Syst (TBioCAS), pp. 1-8, Dec. 2019.
  11. Z. Yan, J. Zhou, W.Wong, "Energy efficient ECG classification with spiking neural network", Biomed Signal Process Control, Vol. 63, pp. 1-7, Aug. 2020.
  12. M. Kachuee, S. Fazeli, and M. Sarrafzadeh. ECG heartbeat classification: A deep transferable representation. Proceedings - IEEE International Conference on Healthcare Informatics,( ICHI), pp. 443-444, Apr. 2018.
  13. A. Y. Hannun, P. Rajpurkar, M. Haghpanahi, et. al., Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine, Vol. 25, No. 1, pp. 65, Jan. 2019. https://doi.org/10.1038/s41591-018-0268-3
  14. B. Grossfeld, Article: "Deep learning vs machine learning: a simple way to understand the difference", Jan. 2020.
  15. D. Soni, "Spiking Neural Networks, the Next Generation of Machine Learning", Towards Data Science Blog, Jan. 2018.
  16. C. Nicholson, "Spiking Neural Networks", Pathmind, A.I.Wiki.
  17. P. U. Diehl and M. Cook, "Unsupervised learning of digit recognition using spike-timing-dependent plastcity", Frontier in Computational Neuroscience, Aug. 2015.
  18. S. R. Kulkarni, B. Rajendran, "Spiking Neural networks for handwritten digit recognition-Supervised learning an network optimization", ELSEVIER: Neural Networks, Vol. 103, pp. 118-127, Jul. 2018. https://doi.org/10.1016/j.neunet.2018.03.019
  19. M. Courbariaux, et al., "Binarized Neural Network: Training Neural Networks with Weights and Activations Constrained to +1 or -1", Machine Learning, pp. 1-11, Mar. 2016.
  20. ECG Dataset : www.physionet.org (retrieved on Dec.17, 2020