Automatic seizure detection in multichannel EEG using McCIT2FIS approach

S. Dora, B. Rangarajan, K. Subramanian, S. Suresh

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

Abstract

In this paper, an automatic seizure detection technique using multichannel EEG is proposed based on Metacognitive Complex-valued Interval Type-2 Fuzzy Inference System (McCIT2FIS). A wavelet chaos theory based feature extraction is employed to extract the features from EEG signal as it can handle the non stationarity in data and Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation (SBMLR) based feature selection is employed to select the most discriminative features. McCIT2FIS is employed to classify the samples as either interictal or ictal EEG segment as it has been shown to be capable of handling noisy data by virtue of Interval Type-2 fuzzy sets, and is good at classification because of its ability to handle complex-valued data. Further, we have also shown that the feature selected using SBMLR can be successfully mapped back to the channels allowing us to identify the epileptogenic regions of the brain. The performance of the McCIT2FIS was also compared with the support vector machines and the results indicate that McCIT2FIS is better capable of detecting seizure based on EEG signals.

Original languageEnglish
Title of host publicationFUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
EditorsAdnan Yazici, Nikhil R. Pal, Hisao Ishibuchi, Bulent Tutmez, Chin-Teng Lin, Joao M. C. Sousa, Uzay Kaymak, Trevor Martin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2015-November
ISBN (Electronic)9781467374286
DOIs
Publication statusPublished - 25 Nov 2015
EventIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
Duration: 2 Aug 20155 Aug 2015

Conference

ConferenceIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
CountryTurkey
CityIstanbul
Period2/08/155/08/15

Keywords

  • complex-valued fuzzy system
  • electroencephalogram
  • interval type-2
  • noisy data
  • Seizure detection

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    Dora, S., Rangarajan, B., Subramanian, K., & Suresh, S. (2015). Automatic seizure detection in multichannel EEG using McCIT2FIS approach. In A. Yazici, N. R. Pal, H. Ishibuchi, B. Tutmez, C-T. Lin, J. M. C. Sousa, U. Kaymak, & T. Martin (Eds.), FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems (Vol. 2015-November). [7338085] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7338085