A 96.2nJ/class Neural Signal Processor with Adaptable Intelligence for Seizure Prediction

📋 论文概要

提出了一种用于癫痫发作预测的神经信号处理器，实现了每分类仅消耗96.2nJ的超低能耗，通过可适配智能技术适应不同患者的脑电特征，解决了植入式闭环神经调控系统对实时、低功耗癫痫检测的需求。

💡 主要创新点

• 提出每分类能耗仅96.2nJ的神经信号处理器，大幅降低功耗以适应植入式应用。
• 采用可适配智能架构，能够在线调整分类参数以匹配不同患者的脑电模式，提高预测准确性。

🏷 关键词

📄 原文摘要

Epilepsy is a common neurodegenerative disease that affects more than 50 million people worldwide. Closed-loop neuromodulation is a promising solution to epileptic seizure control through an implantable device that delivers stimulation when seizures are sensed. Figure 33.2.1 shows an overview of a closed-loop neuromodulation system that includes a neural-signal acquisition unit for extracting EEGs, a neural signal processor for sensing seizures, and a stimulation unit for electrical stimulation. For epileptic states, a seizure onset indicates where a seizure begins, followed by intense brain activity. Several seizure detectors [1][2] having reasonable performance have been proposed to sense seizures after onset. However, patients may still suffer from epileptic syndromes, depending on the severity of the seizures. The syndromes can be eliminated if the seizures can be predicted before onset. This also reduces the amount of required stimulation current,

👥 作者与机构

Yi-Yen Hsieh, Yu-Cheng Lin, Chia-Hsiang Yang

National Taiwan University, Taipei, Taiwan