Integrated Circuits and Systems >

2024 , Vol. 1 >Issue 3: 157 - 165

DOI: https://doi.org/10.23919/ICS.2024.3496614

Original article

URGERS: Ultra-Lightweight Contrastive Learning Encoder for ECG Processor Supporting Both Authentication and Arhythmia Classification

  • JUNLU ZHOU ,
  • LIANG CHANG ,
  • HAODONG FAN ,
  • HAORAN LI ,
  • YANCHENG CHEN ,
  • SHUISHENG LIN ,
  • JUN ZHOU
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  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
+ CORRESPONDING AUTHOR: LIANG CHANG; JUN ZHOU (e-mail: ; ).

Received date: 2024-10-14

  Revised date: 2024-10-31

  Accepted date: 2024-11-05

  Online published: 2025-01-09

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Abstract

Deep learning typically requires large amounts of labeled data and often struggles with generalization, posing challenges for intelligent systems. In the real world, most electrocardiogram (ECG) signals are unlabeled, which limits the use of smart devices in ECG-related applications. Unsupervised learning methods, such as contrastive learning, have emerged as a solution to this constraint. However, most contrastive learning encoders rely on deep neural networks with many parameters, making them unsuitable for hardware implementation. This article introduces a hardware-friendly universal ECG encoder with around 1k parameters based on contrastive learning and a fine-tuning framework for ECG-related tasks. We apply the encoder to a dual-task system for ECG-based arrhythmia classification and authentication, achieving 98.2% and 99.7% accuracy on the MIT-BIH dataset, respectively, with FAR of 0.274 and FRR of 0.707 for authentication. We propose a dynamic averaging template concatenation technique to improve neural network generalization significantly. We also develop an energy-efficient hardware architecture optimized for the entire system, successfully implementing it on an FPGA.

Key words： Arrhythmia; authentication; neural network hardware; unsupervised learning.

Cite this article

JUNLU ZHOU , LIANG CHANG , HAODONG FAN , HAORAN LI , YANCHENG CHEN , SHUISHENG LIN , JUN ZHOU . URGERS: Ultra-Lightweight Contrastive Learning Encoder for ECG Processor Supporting Both Authentication and Arhythmia Classification[J]. Integrated Circuits and Systems, 2024 , 1(3) : 157 -165 . DOI: 10.23919/ICS.2024.3496614

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