A 1.83µJ/Classification Nonlinear Support-VectorMachine-Based Patient-Specific Seizure Classification SoC

📋 论文概要

本文提出了一种基于非线性支持向量机（NLSVM）的患者特异性癫痫发作分类SoC，旨在实现低延迟（<2秒）和高检测率的实时癫痫抑制。该SoC在保持低功耗的同时，克服了之前线性SVM方法检测率低、误报率高的问题。

💡 主要创新点

• 采用非线性SVM代替传统线性SVM，提高了癫痫发作检测的准确率和降低了误报率。
• 实现了1.83µJ/分类的超低能耗，适用于植入式医疗设备。
• 患者特异性设计，可根据个体脑电特征进行定制化分类。

🏷 关键词

📄 原文摘要

To mitigate seizure-affected patients, SoCs [1-3] have been developed 1) to detect electrical onset of seizure seconds before the clinical onset, and 2) to combine the SoC with neurostimulation. In particular, having detection delay of <2s (for real-time suppression) while maintaining high detection rate is challenging [4]. However, [2] had a long latency (13.5s) and [3] suffered from a low detection rate (84.4%) with a high false alarm (max. 14.7%) due to an intermittent limit of the Linear Support Vector Machine (LSVM). In this paper, we present a Non-Linear SVM (NLSVM)-based seizure detection SoC which ensures a >95% detection accuracy, <1% false alarm and <2s latency. Figure 6.2.1 shows the implemented SoC. The AFE is composed of an 8-channel Chopper-Stabilized Capacitive-Coupled IA (CS-CCIA), each channel followed by a variable gain amplifier. The DBE performs patient specific seizure detection

👥 作者与机构

Muhammad Awais Bin Altaf, Judyta Tillak, Yonatan Kifle, Jerald Yoo

Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates