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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(More)
We describe a new strategy to automatically detect the epileptiform activities (IEDs) in the long term 18 channel human electroencephalogram (EEG). Our scheme for detecting epileptic spikes in the EEG is based on a multi resolution, multi-level analysis, which is fast and delivers satisfactory results. The signal on each channel is decomposed into six sub(More)
Various problems associated with epilepsy detection is that the epileptic spike essentially change from one patient to the other and we are in need of trained professional to classify normal brain activity, where the non-pathological events that resemble pathological ones. The aim of this work is the automatic detection of Epileptic and non-Epileptic spike(More)
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