Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
Parallel Algorithm to Analyze the Brain Signals: Application on Epileptic Spikes
Journal of Medical Systems
Remarks about Wavelet Analysis in the EEG Artifacts Detection
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
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In this paper, a spike detection method is introduced. Traditional morphological filter is improved for extracting spikes from epileptic EEG signals and two key problems are addressed: morphological operation design and structure elements optimization. An average weighted combination of open-closing and clos-opening operation, which can eliminate statistical deflection of amplitude, is utilized to separate background EEG and spikes. Then, according to the characteristic of spike component, the structure elements are constructed with two parabolas and a new criterion is put forward to optimize the structure elements. The proposed method is evaluated using normal and epileptic EEG data recorded from 12 test subjects. A comparison between the improved morphological filter, traditional morphological filter and wavelet analysis with Mexican hat function is presented, which indicates that the improved morphological filter is superior in restraining background activities. We demonstrate that the average detection rate of the improved morphological filter is much higher than that of the other two methods, and there is no false detection for normal EEG signals with the proposed method.