Artificial Intelligence Chip Design for High-Speed Cardiac Arrhythmia Classification

Yuan Ho Chen, Ching Tien Wang, Shinn Yn Lin, Chao Sung Lai, Bing Sheu

Research output: Contribution to journalArticlepeer-review

Abstract

An artificial intelligence (AI)-enabled ECG chip (AI-ECG chip) for classifying continuous ECG signals is described. The AI-ECG chip employs a two-stage strategy. It integrates a QRS complex wave detection architecture for signal preprocessing and a two-layer deep-learning network for post-processing. TSMC 180nm180nm complementary metal-oxide semiconductor fabrication process was used to produce the AI-ECG chip, which can be operated at a maximum frequency of 26.3MHz26.3MHz while consuming 3.11mW3.11mW. Despite its compact 1.41 - mm21.41-mm2 size. The AI-ECG chip can achieve arrhythmia detection accuracy of 90.56%. A salient feature of this chip is the ability to identify up to four different arrhythmias, thus offering a more extensive diagnostic range than most comparable chips. In summary, the AI-ECG chip achieves great balance among chip size, power efficiency, and detection capabilities. It is an attractive solution for portable ECG monitoring systems.

Original languageEnglish
Pages (from-to)29-35
Number of pages7
JournalIEEE Nanotechnology Magazine
Volume17
Issue number6
DOIs
StatePublished - 1 Dec 2023

Keywords

  • Convolutional neural network
  • electrocardiogram (ECG)
  • QRS detection
  • very -large-scale integration (VLSI)

Fingerprint

Dive into the research topics of 'Artificial Intelligence Chip Design for High-Speed Cardiac Arrhythmia Classification'. Together they form a unique fingerprint.

Cite this