Zhang Chen, Bin Altaf Muhammad Awais, Yoo Jerald
IEEE J Biomed Health Inform. 2016 Jul;20(4):996-1007. doi: 10.1109/JBHI.2016.2553368. Epub 2016 Apr 12.
This paper presents the design of an area- and energy-efficient closed-loop machine learning-based patient-specific seizure onset and termination detection algorithm, and its on-chip hardware implementation. Application- and scenario-based tradeoffs are compared and reviewed for seizure detection and suppression algorithm and system which comprises electroencephalography (EEG) data acquisition, feature extraction, classification, and stimulation. Support vector machine achieves a good tradeoff among power, area, patient specificity, latency, and classification accuracy for long-term monitoring of patients with limited training seizure patterns. Design challenges of EEG data acquisition on a multichannel wearable environment for a patch-type sensor are also discussed in detail. Dual-detector architecture incorporates two area-efficient linear support vector machine classifiers along with a weight-and-average algorithm to target high sensitivity and good specificity at once. On-chip implementation issues for a patient-specific transcranial electrical stimulation are also discussed. The system design is verified using CHB-MIT EEG database [1] with a comprehensive measurement criteria which achieves high sensitivity and specificity of 95.1% and 96.2%, respectively, with a small latency of 1 s. It also achieves seizure onset and termination detection delay of 2.98 and 3.82 s, respectively, with seizure length estimation error of 4.07 s.
本文介绍了一种基于机器学习的面积和能量高效的闭环患者特异性癫痫发作起始和终止检测算法的设计及其片上硬件实现。针对癫痫检测和抑制算法及系统（包括脑电图（EEG）数据采集、特征提取、分类和刺激），比较并回顾了基于应用和场景的权衡。对于训练癫痫发作模式有限的患者进行长期监测，支持向量机在功耗、面积、患者特异性、延迟和分类准确率之间实现了良好的权衡。还详细讨论了在用于贴片式传感器的多通道可穿戴环境中进行EEG数据采集的设计挑战。双检测器架构结合了两个面积高效的线性支持向量机分类器以及加权平均算法，以同时实现高灵敏度和良好的特异性。还讨论了患者特异性经颅电刺激的片上实现问题。使用CHB-MIT EEG数据库[1]对系统设计进行了验证，采用了综合测量标准，分别实现了95.1%和96.2%的高灵敏度和特异性，延迟仅为1秒。它还分别实现了癫痫发作起始和终止检测延迟2.98秒和3.82秒，癫痫发作长度估计误差为4.07秒。
