一种用于闭环癫痫检测与刺激系统的新型实时阈值算法。

A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System.

作者信息

Wang Liang-Hung, Zhang Zhen-Nan, Xie Chao-Xin, Jiang Hao, Yang Tao, Ran Qi-Peng, Fan Ming-Hui, Kuo I-Chun, Lee Zne-Jung, Chen Jian-Bo, Chen Tsung-Yi, Chen Shih-Lun, Abu Patricia Angela R

机构信息

The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.

College of Biological Science and Engineering, Fuzhou University, Fuzhou 350108, China.

出版信息

Sensors (Basel). 2024 Dec 24;25(1):33. doi: 10.3390/s25010033.

Abstract

Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures. This study proposes a novel closed-loop epilepsy detection system and stimulation control chip. A time-domain detection algorithm based on amplitude, slope, line length, and signal energy characteristics is introduced. A new threshold calculation method is proposed; that is, the threshold is updated by means of the mean and standard deviation of four consecutive eigenvalues through parameter combination. Once a seizure is detected, the system begins to control the stimulation of a two-phase pulse current with an amplitude and frequency of 34 μA and 200 Hz, respectively. The system is physically designed on the basis of the UMC 55 nm process and verified by a field programmable gate array verification board. This research is conducted through innovative algorithms to reduce power consumption and the area of the circuit. It can maintain a high accuracy of more than 90% and perform seizure detection every 64 ms. It is expected to provide a new treatment for patients with epilepsy.

摘要

癫痫作为一种常见的脑部疾病,给全球患者带来了巨大的痛苦和压力。目前,主要的治疗方法有药物治疗、手术治疗和电刺激疗法。电刺激最近已成为一种减少症状性癫痫发作的替代治疗方法。本研究提出了一种新型的闭环癫痫检测系统和刺激控制芯片。介绍了一种基于幅度、斜率、线长和信号能量特征的时域检测算法。提出了一种新的阈值计算方法,即通过参数组合,利用四个连续特征值的均值和标准差来更新阈值。一旦检测到癫痫发作,系统就开始控制刺激一个幅度为34 μA、频率为200 Hz的两相脉冲电流。该系统基于UMC 55 nm工艺进行物理设计,并通过现场可编程门阵列验证板进行验证。本研究通过创新算法来降低功耗和电路面积。它可以保持超过90%的高精度,并每64毫秒进行一次癫痫发作检测。有望为癫痫患者提供一种新的治疗方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18fa/11723055/166e3f8d7914/sensors-25-00033-g001.jpg

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