多通道腦波特徵抽取及分析之癲癇預測系統

Abstract

癲癇是一種常見的慢性神經疾病，並且會有不定時的發作情形。顛顯發作時病人會短暫失去肢體控制並導致生命危險。目前有關癲癇之研究及診斷多數利用腦波圖(Electroencephalogram)。腦波圖可以用不同的顯示方法被呈現，其中兩種為單極點訊號 (Unipolar)和雙極點訊號 (Bipolar)。傳統腦波訊號分析大多利用單極點訊號作為基礎，但醫師在診斷顛癇時時常利用雙極點訊號來呈現腦波圖。因此我們也把雙極點訊號拿來作為辨識系統之參考數據。我們設計了一列對於雙極點訊號之訊號處理及特徵抽取方法希望能夠改善目前現有之自動化癲癇診斷系統。在訊號處理方面我們利用了小波轉換(Wavelet Transform)將主要不同腦波頻帶抽取出來。在特徵抽取上我們利用似熵 (Approximate entropy)及種總變差(Total variation)來顯示出規則與不規則之腦波現象。在特徵排序及選擇我們採用了基因演算法 (Genetic Algorithm)和費雪分數法 (Fisher Score)。最後再利用支持向量機(Support Vector Machine)來當我們的分類器。

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Seizure episodes can cause temporal paralysis of the body, which can lead to severe injuries. Electroencephalogram (EEG) is a tool commonly used for analyzing brain activity and diagnosing brain disorders. EEG can be presented under different montage schemes. This study focuses on two of the montage schemes; unipolar montage and bipolar montage. Traditionally, the most commonly used montage for automated EEG analysis is unipolar. We experiment with incorporating bipolar EEG montage for creating a classification system to classify different epileptic wave forms. A series of functions were designed for bipolar EEG montage. We used wavelet transform (WT) to decompose EEG signal into its primary sub-bands. We use Approximate Entropy and Total Variation as features designed specifically for spike and seizure detection. We used Genetic Algorithm and Fisher Score to rank and selected most influential features for classifier. Finally we use multi-class Support Vector Machine as our classifier.