The Study of Using EEG Analysis in Epilepsy Detection

• Main Authors: Yu-Jhih Huang, 黃宇志
• Other Authors: Hsiu-Sen Chiang
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/55mmr9

碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 103 === Epilepsy is a common neurological diseases, it’s a disease of the cranial nerves. To suffer epilepsy of rate is second only to cerebrovascular attacks. Epilepsy may cause seizures, loss consciousness, impacted on intelligence, even into shock. The electroencephalogram (EEG) has been used as a tool for diagnosing epilepsy. Record by brain waves, can detect whether the subjects were suffering from epilepsy, and as an objective basis to judge. Diagnosis of epilepsy is a way visual observation by Professional doctors, it wasted time and costs. This study developed a model for Diagnosis of epilepsy through utilizing the discrete wavelet transform, minimize entropy principle approach, and associative Petri nets. This study also used decision tree, support vector machine, neural network, Bayes net, naïve Bayes, and tree augmented naïve Bayes compared to accuracy rate of associative Petri nets. Result, accuracy rate of associative Petri nets is 93.8%.