北京生物醫(yī)學工程

基于AR模型和Lempel-Ziv復雜度的癲癇發(fā)作預報

Epileptic seizure prediction based on AR model and LZC

作者：韓敏曹占吉孫磊磊洪曉軍

單位：大連理工大學電子信息與電氣工程學部(遼寧大連 116023)

關(guān)鍵詞：癲癇；腦電信號；自回歸模型；Lempel-Ziv復雜度；發(fā)作預報

分類號：

出版年·卷·期（頁碼）：2012·31·3（273-277）

摘要：

目的癲癇是由多種病因引起的慢性腦功能障礙綜合征，及時的發(fā)作預報，對于建立新的治療方法和改善患者的生活質(zhì)量有著至關(guān)重要的作用。目前大部分腦電分析算法存在計算速度慢、適應性差等問題，無法滿足癲癇腦電發(fā)作預報的要求。方法本文應用自回歸模型對腦電信號進行特征提取，支持向量機（support vector machine, SVM）作腦電各個時期分類器，并與Lempel-Ziv復雜度分析計算相結(jié)合，準確識別發(fā)作前期，以實現(xiàn)癲癇的發(fā)作預報。結(jié)果應用弗萊堡大學數(shù)據(jù)對上述方法的有效性進行驗證。仿真結(jié)果表明，該方法得到的發(fā)作漏檢率、誤報率較低，預報提前時間較長。結(jié)論將AR模型和Lempel-Ziv復雜度相結(jié)合，對癲癇發(fā)作預報的實現(xiàn)，有一定參考價值和意義。

Objective Epilepsy is a chronic brain dysfunction syndrome caused by many diseases. The predictions of epilepsy seizure are significant for both the establishment of new treatment methods and the improvement of the patients’ life qualities. The current EEG analysis algorithm cannot meet the requirement of epileptic seizure prediction for the slow computation and the poor adaptability. Methods This paper applies autoregressive(AR) model for feature extraction, a support vector machine as a classifier, and combines Lempel-Ziv complexity(LZC) to identify preictal accurately. Results Using the data from Freiburg University, the simulation results show that the methods used in this paper achieve a lower false alarm rate, a lower failed reporting rate and a longer lead time. Conclusions This paper provides references for the realization of the epileptic seizure prediction by combining AR model and LZC.

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