Non-invasive mobile electroencephalography (EEG) acquisition systems have been utilized for long-term monitoring of seizures, yet they suffer from limited battery life. Resistive random access memory (RRAM) is widely used in computing-in-memory(CIM) systems, which offers an ideal platform for reducing the computational energy consumption of seizure prediction algorithms, potentially solving the endurance issues of mobile EEG systems. To address this challenge, inspired by neuronal mechanisms, we propose a RRAM-based bio-inspired circuit system for correlation feature extraction and seizure prediction. This system achieves a high average sensitivity of 91.2% and a low false positive rate per hour (FPR/h) of 0.11 on the CHB-MIT seizure dataset. The chip under simulation demonstrates an area of approximately 0.83 mm2 and a latency of 62.2 {\mu}s. Power consumption is recorded at 24.4 mW during the feature extraction phase and 19.01 mW in the seizure prediction phase, with a cumulative energy consumption of 1.515 {\mu}J for a 3-second window data processing, predicting 29.2 minutes ahead. This method exhibits an 81.3% reduction in computational energy relative to the most efficient existing seizure prediction approaches, establishing a new benchmark for energy efficiency.

翻译：非侵入式移动脑电图采集系统已被用于癫痫发作的长期监测，但其电池续航能力有限。阻变随机存取存储器广泛应用于存内计算系统，为降低癫痫预测算法的计算能耗提供了理想平台，有望解决移动脑电图系统的续航问题。受神经元机制启发，我们提出一种基于RRAM的生物启发电路系统，用于相关性特征提取与癫痫发作预测。该系统在CHB-MIT癫痫数据集上实现了91.2%的平均灵敏度与每小时0.11的低误报率。仿真芯片面积约为0.83 mm²，延迟为62.2 μs。特征提取阶段功耗为24.4 mW，癫痫预测阶段为19.01 mW，对3秒时间窗数据进行处理的累计能耗为1.515 μJ，可提前29.2分钟完成预测。相较于现有最高效的癫痫预测方法，本方案计算能耗降低81.3%，为能效设立了新基准。