Separation of Background and Spiky Activity in Subdural EEG Based on Morphological Analysis and Wavelet Transform

運用形態分析和小波轉換將腦波(EEG)區分為背景訊號與尖峰訊號

逄霖生(Lin-Sen Pon)

《華岡工程學報》 19期 (2005/06) Pp. 73-82

https://doi.org/10.29987/CCUHKJE.200506.0007

摘要

本研究使用了mathematical morphology和wavelet transform的模型，此模型可以將非線性、非stationary的腦波訊號，區分為兩部份：(1)正常的背景訊號，與(2)不正常的尖峰訊號。腦波訊號(EEG)是由一位從小就患有癲癇的病童的大腦皮質表面直接記錄下來的資料(Subdural EEG)，此EEG訊號包含了變化較慢的正常腦波活動(background activity)與不正常的癲癇訊號。這種癲癇訊號狀似一個尖峰故此訊號殼稱為尖峰訊號(spike)，通稱為spiky activity。這兩類的訊號的型態有很大的不同，本文以此差異提出用數學的型態分析方法(mathematical morphology)，對兩種混合的腦波訊號加以分離。不正常的癲癇訊號發生的時問與長短沒有固定的模式，針對這類的訊號，本文提出利用多重解析變換的小波轉換(multi-resolution wavelet transform)來分析這類型態類似、但大小區間變異很大的癲癇訊號。本文包含數學理論的說明簡介，完整的癲癇訊號分離步驟程序，並包含實驗結果與討論。

關鍵字

腦波正常背景訊號 ； 腦電波 ； 癲癇 ； 型態學 ； 多重訊號解析 ； 尖峰訊號 ； 暫態 ； 小波轉換

並列摘要

A new method to separate two components, background and spiky activity, from subdural electroencephalogram (EEG) is presented in this paper. The spiky activity is characterized by transient waveforms whose spectra are overlapped with the background activity which is the dominant activity with relatively slow amplitude. Both components are non-stationary. It is found that the two components are different in their morphological characteristics. Based on this difference we apply nonlinear morphological operations to scaling and wavelet coefficients so as to obtain an accurate, separated reconstruction of the two components. This is based on repartitioning and manipulating energy in the localized scaling and wavelet coefficients so that key characteristics of the spiky activity appear in one sub-signal while characteristics of the background activity appears in another sub-signal. By using a simple morphological operation on both scaling coefficients and wavelet coefficients, our preliminary investigation produced promising results.

並列關鍵字

Background Activity ； EEG ； Epilepsy ； Mathematical Morphology ； Multi-resolution Analysis ； Spikes ； Transient Activity ； Wavelet Transform