Memristive Reservoir Computing Circuit for Real-Time Prediction of Epilepsy

The prediction of epileptic seizures can significantly improve patients' quality of life by enabling timely preventive interventions. However, realizing automated real-time prediction on edge hardware remains challenging due to high computational complexity, inefficient temporal signal processing, and the von Neumann bottleneck. In this work, we propose a memristor-based multi-stage reservoir computing architecture that jointly addresses algorithmic and hardware limitations. Volatile memristors are employed in reservoir modules to perform nonlinear temporal feature extraction, avoiding error accumulation issues commonly observed in recurrent neural networks. Non-volatile memristor crossbar arrays are further integrated to implement in-memory analog multiply-accumulate operations, significantly reducing data movement and improving hardware efficiency. Owing to the proposed multi-stage structure, high prediction accuracy is achieved with only 1,700 trainable parameters. Moreover, comprehensive hardware-aware evaluations are conducted, including input noise injection, device-to-device and cycle-to-cycle variations to assess robustness against memristor non-idealities. Results demonstrate that the proposed system achieves over 97% accuracy in simulation and exceeds 95% accuracy in hardware experiments, while maintaining stable performance under substantial noise, making it a promising low-power solution for real-time seizure prediction on edge platforms.