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

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dc.contributor.authorIndiradevi, KP
dc.contributor.authorElias, E
dc.contributor.authorSathidevi, PS
dc.contributor.authorNayak, SD
dc.contributor.authorRadhakrishnan, K
dc.date.accessioned2017-03-10T03:25:10Z
dc.date.available2017-03-10T03:25:10Z
dc.date.issued2008
dc.description.abstractWe 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.
dc.identifier.citation38 ,7;805-816en_US
dc.identifier.uri10.1016/j.compbiomed.2008.04.010
dc.identifier.urihttps://dspace.sctimst.ac.in/handle/123456789/9177
dc.publisherCOMPUTERS IN BIOLOGY AND MEDICINE
dc.subjectLife Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology
dc.titleA multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram
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