Interictal Noise Cancellation Based on Combination of ICA-based and Wavelet-based Denoising Approaches

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Zakizadeh, Mohammad | 2014

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Type of Document: M.Sc. Thesis
Language: English
Document No: 46539 (55)
University: Sharif University of Technology, International Campus, Kish Island
Advisor(s): Shamsollahi, Mohammad Bagher
Abstract:
Interictal EEG signals are very critical in diagnosis of epilepsy. Analysis of interictal EEG signals is very challenging due to contamination by various undesired signals like background EEG, muscular activity, noise, etc. Thus denoising of interictal signals has been an active research field in recent years. Primary purpose of this thesis is to denoise interictal EEG signals by using different combinations of ICA-based and wavelet denoising approaches. Then a new direction is pursued by using Morphological Component Analysis (MCA) which is a method for solving source separation problems based on morphological diversity of sources. Afterward MCA is modified by considering more prior information about epileptic components. Finally different methods are compared by using quantitative measures and also observing the impact of denoising procedure on source localization. Results show that in general, modified MCA outperforms generic MCA and different combinations of ICA-based and Wavelet denoising methods
Keywords:
Wavelets ; Independent Component Analysis (ICA) ; Denoising ; Wavelet Analysis ; Interictal Signals ; Morphological Component Analysis

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