A Memristor‐Based Event‐Driven Reservoir Computing System for Cardiac Arrhythmia Detection

Cardiac arrhythmia detection is crucial for preventing heart‐related emergencies with wearable electrocardiograph (ECG) monitoring and recording. However, traditional systems face significant challenges in terms of power consumption and real‐time data processing. This article presents a novel memristor‐assisted event‐driven system for low‐power and efficient ECG signal processing. The system incorporates a memristor‐based level‐crossing event encoder, reducing redundant data by up to 88.23% compared to traditional Nyquist sampling methods. These event streams are then processed by a cascaded memristor‐based reservoir layer, which dynamically maps temporal patterns into a high‐dimensional feature space, enabling robust and efficient feature extraction. Additionally, a memristor crossbar array is employed to accelerate matrix computations for neuromorphic network classification. Leveraging the inherent low‐power and non‐linear dynamics of memristor devices, the proposed system achieves high arrhythmia detection accuracy (up to 98.43%) with an average power consumption of 5.26 mW, demonstrating its potential for real‐time, wearable edge AI cardiac monitoring applications.