A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram

Authors:
K. P. Indiradevi;Elizabeth Elias;P. S. Sathidevi;S. Dinesh Nayak;K. Radhakrishnan
Affiliations:
Department of Electronics and Communication Engineering, National Institute of Technology, NIT Campus, Calicut 673601, Kerala, India;Department of Electronics and Communication Engineering, National Institute of Technology, NIT Campus, Calicut 673601, Kerala, India;Department of Electronics and Communication Engineering, National Institute of Technology, NIT Campus, Calicut 673601, Kerala, India;R. Madhavan Nayar Center for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute for Medical Science and Technology, Trivandrum, Kerala, India;R. Madhavan Nayar Center for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute for Medical Science and Technology, Trivandrum, Kerala, India
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

We describe a strategy to automatically identify epileptiform activity in 18-channel human electroencephalogram (EEG) based on a multi-resolution, multi-level analysis. The signal on each channel is decomposed into six sub-bands using discrete wavelet transform. Adaptive threshold is applied on sub-bands 4 and 5. The spike portion of EEG signal is then extracted from the raw data and energy of the signal for locating the exact location of epileptic foci is determined. The key points of this process are identification of a suitable wavelet for decomposition of EEG signals, recognition of a proper resolution level, and computation of an appropriate dynamic threshold.