A Novel Real-Time Threshold Algorithm for Closed-Loop Epilepsy Detection and Stimulation System - Liang-Hung Wang; Zhen-Nan Zhang; Chao-Xin Xie; Hao Jiang; Tao Yang; Qi-Peng Ran; Ming-Hui Fan; I-Chun Kuo; Zne-Jung Lee; Jian-Bo Chen; Tsung-Yi Chen; Shih-Lun Chen; et al. (Sensors)
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.