A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network

Yuan Ho Chen, Szi Wen Chen, Pei Jung Chang, Hsin Tung Hua, Shinn Yn Lin, Rou Shayn Chen

Research output: Contribution to journalJournal Article peer-review

10 Scopus citations

Abstract

The heart is one of the human body's vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to design and realize an artificial intelligence (AI)-based abnormal heart beat detection with applications for early detection and timely treatment for heart diseases. A convolutional neural network (CNN) was employed to achieve a fast and accurate identification. In order to meet the requirements of the modularity and scalability of the circuit, modular and efficient processing element (PE) units and activation function modules were designed. The proposed CNN was implemented using a TSMC 0.18 μm CMOS technology and had an operating frequency of 60 MHz with chip area of 1.42 mm2 and maximum power dissipation of 4.4 mW. Furthermore, six types of ECG signals drawn from the MIT-BIH arrhythmia database were used for performance evaluation. Results produced by the proposed hardware showed that the discrimination rate was 96.3% with high efficiency in calculation, suggesting that it may be suitable for wearable devices in healthcare.

Original languageEnglish
Article number796
JournalSensors
Volume22
Issue number3
DOIs
StatePublished - 21 01 2022

Keywords

  • convolutional neural network (CNN)
  • electrocardiogram (ECG)
  • very large scale integration implementation (VLSI)

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