A 1.83µJ/classification nonlinear support-vector-machine-based patient-specific seizure classification SoC
A 1.83µJ/classification nonlinear support-vector-machine-based patient-specific seizure classification SoC
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1.83μJ/分类基于非线性支持向量机的患者特定癫痫发作分类 SoC
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Jerald Yoo
中科院分区：
文献类型：
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作者：
Muhammad Awais Bin Altaf;J. Tillak;Yonatan Kifle;Jerald Yoo
为了减轻癫痫发作影响的患者，已经开发了 SoC [1-3]：1) 在临床发作前几秒检测癫痫发作的电发作，2) 将 SoC 与神经刺激相结合。特别是，在保持高检测率的同时检测延迟<2s（用于实时抑制）具有挑战性[4]。然而，由于线性支持向量机（LSVM）的间歇性限制，[2]的延迟时间较长（13.5秒），[3]的检测率较低（84.4％），误报率较高（最大14.7％）。在本文中，我们提出了一种基于非线性 SVM (NLSVM) 的癫痫检测 SoC，可确保 >95% 的检测精度、<1% 的误报和 <2 秒的延迟。
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.