基於頭皮腦波零越點區間特徵之癲癇發作預測

Epileptic Seizure Prediction Based on Zero-Crossing Interval Features of Scalp EEG Signals

陳柏仲(Bo-Jhong Chen)

指導教授 ： 歐陽振森

義守大學/電機資訊學院/資訊工程學系/碩士(2017年)

摘要

本研究結合頭皮腦波零越點區間與心率變異分析，提出一種癲癇發作預測方法。對於癲癇患者的頭皮腦波，先取得其發作前與發作間之數個腦波分段，並計算每個分段之零越點區間序列。接著，利用心率變異分析以時間視窗滑動的方式計算零越點區間序列之多個特徵。再來，運用統計檢定評估每個特徵對於發作前與發作間狀態分類之鑑別性。最後，以隨機決策森林、貝氏分類器、最近鄰居法、支持向量機、線性識別分析等五種常用的分類建模方法分別建立發作前與發作間狀態分類模型，並進行分類結果比較。

關鍵字

正零越點區間 ； 零越點 ； 心率異變率 ； 分段 ； 分類 ； 癲癇預測

並列摘要

In this study, we propose an approach of epileptic seizure prediction by combining the zero-crossing intervals of scalp EEG signals and heart rate variability analysis. In this study, we propose an online fuzzy extreme learning machine based on the recursive singular value decomposition for improving the fuzzy extreme learning machine, and therefore making it applicable for solving online learning problems in classification or regression modeling. Like the original fuzzy extreme learning machine, our approach randomly assigns values to weights of fuzzy membership functions in the hidden layer. However, the Moore-Penrose pseudoinverse is replaced with the recursive singular value decomposition for calculating the optimal weights corresponding to the output layer. Compared with the original fuzzy extreme learning machine, our approach is applicable for the online learning of classification or regression modeling and produces the same modeling accuracy. Moreover, our approach possesses the better modeling accuracy and stability than the other approach, namely, online sequential learning algorithm.

並列關鍵字

Positive zero-crossing ； Zero-crossing ； HRV ； Seizure prediction

參考文獻

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