Combination of EEG Spectrum and Complexity Analysis for Robust Online Epileptic Seizure Detection

• Main Authors: Hsu-chuan Wang, 王敘全
• Other Authors: Sheng-fu Liang
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/28980828820306405178

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 96 === Epilepsy is one of the most common neurological disorders, approximately 1% of people in the world have epilepsy, 25% of epilepsy patients cannot be treated sufficiently by any available therapy. Epilepsy is caused by abnormal discharges in the brain, thus EEG has been an especially valuable clinical tool for the evaluation, detection, and treatment of epilepsy. Through EEG recordings, a number of systems which can release drug or give an electrical stimulation to suppress the seizures have been developed and under clinical operation for years. However, a robust device has not yet been developed which compute quickly and fast enough to action to meet immediately pathological changes of different types of seizures in human. In this paper, we propose a fast and reliable epilepsy detection method based on the complexity analysis and spectrum analysis. We propose complexity measure ApC and combine it with selected frequency bands power as the features for detecting seizures. An early seizure detection method is also presented which can detect seizures in a short time while seizures onset. Three different types of seizures are used for testing the detection performance. By the experiment result, the proposed epilepsy detection method can detect seizures in accuracy above 95% with a short detection delay 0.36-0.69 sec.