Physical Reservoir Computing for Real-Time Electrocardiogram Arrhythmia Detection Through Controlled Ion Dynamics in Electrochemical Random-Access Memory

Abstract

From the perspective of developing low-power mobile healthcare devices capable of real-time electrogram diagnosis, memristor-based physical reservoir computing (PRC) offers a promising alternative to conventional deep neural network (DNN)-based systems. Here, real-time electrocardiogram (ECG) monitoring and arrhythmia detection are demonstrated using electrochemical random-access memory (ECRAM)-based PRC. ECRAM devices provide the millisecond-range temporal resolution required for bio-potential signals like ECG. Through material and process engineering, it is identified that higher ionic conductivity (σ_ion) in the electrolyte layer and lower ionic diffusivity (D_ion) in the channel layer are crucial for achieving non-linear dynamics and fading memory characteristics. In addition, LaF₃/WO_x-based ECRAM exhibits low-power operation (≈300 pW spike⁻¹) with minimal cycle-to-cycle (CTC) variation (<10%). Arrhythmia detection tests confirmed the feasibility of real-time ECG monitoring, achieving a high classification accuracy of 93.04% with a 50-fold reduction in training parameters compared to DNN-based systems. Therefore, the developed LaF₃/WO_x-based ECRAM with engineering guidelines of ion dynamics makes a significant contribution to mobile healthcare systems for electrogram diagnosis.