Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers

Mojtaba Bandarabadi, Antonio Dourado, Cesar A. Teixeira, Theoden I. Netoff, Keshab K. Parhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

This paper presents the results of our study on finding a lower complexity and yet a robust seizure prediction method using intracranial electroencephalogram (iEEG) recordings. We compare two classifiers: a low-complexity Adaboost and the more complex support vector machine (SVM). Adaboost is a linear classier using decision stumps, and SVM uses a nonlinear Gaussian kernel. Bipolar and/or time-differential spectral power features of different sub-bands are extracted from the iEEG signal. Adaboost is used to simultaneously classify as well as rank the features. Eliminating the low discriminating features reduces computational complexity and power consumption. The top features selected by Adaboost were also used as a feature set for SVM classification. The outputs of classifiers are regularized by applying a moving-average window and a threshold is used to generate alarms. The proposed methods were applied on 8 invasive recordings selected from the EPILEPSIAE database, the European database of EEG seizure recordings. Doublecross validation is used by separating data sets for training and optimization from testing. The key conclusion is that Adaboost performs slightly better than SVM using a reduced feature set on average with significantly less complexity resulting in a sensitivity of 77.1% (27 of 35 seizures in 873h recordings) and a false alarm rate of 0.18 per hour.

Original languageEnglish (US)
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages6305-6308
Number of pages4
DOIs
StatePublished - Oct 31 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period7/3/137/7/13

Keywords

  • Adaboost
  • Classification
  • Epilepsy
  • Power Spectral Density
  • Seizure Prediction
  • Support Vector Machine

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    Bandarabadi, M., Dourado, A., Teixeira, C. A., Netoff, T. I., & Parhi, K. K. (2013). Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 6305-6308). [6610995] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6610995