Please use this identifier to cite or link to this item: http://dspace.sctimst.ac.in/jspui/handle/123456789/9177
Title: A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram
Authors: Indiradevi, KP
Elias, E
Sathidevi, PS
Nayak, SD
Radhakrishnan, K
Keywords: Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology
Issue Date: 2008
Publisher: COMPUTERS IN BIOLOGY AND MEDICINE
Citation: 38 ,7;805-816
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. (c) 2008 Elsevier Ltd. All rights reserved.
URI: 10.1016/j.compbiomed.2008.04.010
http://dspace.sctimst.ac.in/jspui/handle/123456789/9177
Appears in Collections:Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.