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 language | English |
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Pages (from-to) | 29-35 |
Number of pages | 7 |
Journal | IEEE Nanotechnology Magazine |
Volume | 17 |
Issue number | 6 |
DOIs | |
State | Published - 1 Dec 2023 |
Keywords
- Convolutional neural network
- electrocardiogram (ECG)
- QRS detection
- very -large-scale integration (VLSI)