Denoising of ictal EEG data using semi-blind source separation methods based on time-frequency priors

Hajipour Sardouie, S ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1109/JBHI.2014.2336797
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2015
  4. Abstract:
  5. Removing muscle activity from ictal ElectroEncephaloGram (EEG) data is an essential preprocessing step in diagnosis and study of epileptic disorders. Indeed, at the very beginning of seizures, ictal EEG has a low amplitude and its morphology in the time domain is quite similar to muscular activity. Contrary to the time domain, ictal signals have specific characteristics in the time-frequency domain. In this paper, we use the time-frequency signature of ictal discharges as a priori information on the sources of interest. To extract the time-frequency signature of ictal sources, we use the Canonical Correlation Analysis (CCA) method. Then, we propose two time-frequency based semi-blind source separation approaches, namely the Time-Frequency-Generalized EigenValue Decomposition (TF-GEVD) and the Time-Frequency-Denoising Source Separation (TF-DSS), for the denoising of ictal signals based on these time-frequency signatures. The performance of the proposed methods is compared with that of CCA and Independent Component Analysis (ICA) approaches for the denoising of simulated ictal EEGs and of real ictal data. The results show the superiority of the proposed methods in comparison with CCA and ICA
  6. Keywords:
  7. Canonical Correlation Analysis (CCA) ; Denoising Source Separation (DSS) ; ElectroEncephaloGram (EEG) ; Epileptic seizure ; Fast ictal activity ; Generalized EigenValue Decomposition (GEVD) ; Semi-blind source separation ; Biomedical signal processing ; Blind source separation ; Correlation methods ; Eigenvalues and eigenfunctions ; Frequency domain analysis ; Independent component analysis ; Epileptic seizures ; Time domain analysis ; Pathophysiology ; Procedures ; Signal processing ; Adult ; Algorithms ; Brain ; Electroencephalography ; Epilepsy ; Humans ; Signal Processing, Computer-Assisted ; Young Adult
  8. Source: IEEE Journal of Biomedical and Health Informatics ; Volume 19, Issue 3 , July , 2015 , Pages 839-847 ; 21682194 (ISSN)
  9. URL: http://ieeexplore.ieee.org/document/6866858/?arnumber=6866858