A 1.69$\mu$J Highly Robust Cardiac Arrhythmia Monitoring Processor With Triple-Adaptive QRS Detector and Medically...

The detection of arrhythmias is crucial in monitoring cardiac health. However, electrocardiogram (ECG) signals obtained from wearable devices are often compromised by noise, including electrode motion artifacts, baseline wander, and muscle artifacts. This paper addresses these challenges by proposing a highly robust cardiac health monitoring processor featuring a cascaded triple-adaptive QRS detector and medically driven feature-fusion hybrid neural networks (HNN) for arrhythmia classification. The QRS detector uses a self-adaptive triple-threshold mechanism that dynamically correlates duration, RR interval, and error correction thresholds, allowing it to accurately identify QRS complex features in noisy signals, facilitated by event-driven sampling. The HNN arrhythmia classifier combines long short-term memory (LSTM) and artificial neural network architectures with three medically driven pathological feature fusion, achieving improved computational efficiency. The prototype is fabricated using the 65-nm CMOS process. The results reveal three findings. First, the total and dynamic power are 2.53 $\mu$W and 0.072 $\mu$W, respectively, and the all-digital implementation achieves the 0.99 mm${}^{2}$ area. Second, the average R-peak detection sensitivity/precision rates exceed 97.38%/97.08% on the MIT-BIH Noise Stress Test Database, and inter-patient classification accuracy exceeds 90.1% on the MIT-BIH Arrhythmia Database under a 6 dB signal-to-noise ratio (SNR). Third, the system achieves low computational complexity with only 2063 parameters and 5.5 KB of SRAM.