Artificial Intelligence Chip Design for High-Speed Cardiac Arrhythmia Classification

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 $\text{180}~nm$ complementary metal-oxide semiconductor fabrication process was used to produce the AI-ECG chip, which can be operated at a maximum frequency of $\text{26.3}~MHz$ while consuming $\text{3.11}~mW$ . Despite its compact $1.41 - m{m^2}$ 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.