Effect of filtering with time domain features for the detection of epileptic seizure from EEG signals

Abstract

Pattern recognition plays an important role in the detection of epileptic seizure from electroencephalogram (EEG) signals. In this pattern recognition study, the effect of filtering with the time domain (TD) features in the detection of epileptic signal has been studied using naive Bayes (NB) and supports vector machines (SVM). It is the first time the authors attempted to use TD features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived from the filtered and unfiltered EEG data, and performance of these features is studied along with mean absolute value (MAV) which has been already attempted by the researchers. The other TD features which are attempted by the researchers such as standard deviation (SD) and average power (AVP) along with MAV are studied. A comparison is made in effect of filtering and without filtering for the University of Bonn database using NB and SVM for the TD features attempted first time along with MAV. The effect of individual and combined TD features is studied and the highest classification accuracy obtained in using direct TD features would be 99.87%, whereas it is 100% with filtered EEG data. The raw EEG data can be segmented and filtered using the fourth-order Butterworth band-pass filter.

Keywords:

• Epileptic seizure
• waveform length
• number of zero-crossings
• number of slope-sign changes
• naive Bayes
• support vector machines

Acknowledgements

The authors would like to thank University of Bonn, Germany, for their database and also thank our VIT University for providing all help and support for this study.

Disclosure statement

No potential conflict of interest was reported by the authors.