Automatic seizure detection in multichannel EEG using McCIT2FIS approach

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

doi:10.1109/FUZZ-IEEE.2015.7338085

Automatic seizure detection in multichannel EEG using McCIT2FIS approach

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
Editors	Adnan Yazici, Nikhil R. Pal, Hisao Ishibuchi, Bulent Tutmez, Chin-Teng Lin, Joao M. C. Sousa, Uzay Kaymak, Trevor Martin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2015-November
ISBN (Electronic)	9781467374286
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2015.7338085
Publication status	Published (in print/issue) - 25 Nov 2015
Event	IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey Duration: 2 Aug 2015 → 5 Aug 2015

Conference

Conference	IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Country/Territory	Turkey
City	Istanbul
Period	2/08/15 → 5/08/15

Keywords

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

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10.1109/FUZZ-IEEE.2015.7338085

Cite this

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). Article 7338085 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7338085

Dora, S. ; Rangarajan, B. ; Subramanian, K. et al. / Automatic seizure detection in multichannel EEG using McCIT2FIS approach. FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. editor / Adnan Yazici ; Nikhil R. Pal ; Hisao Ishibuchi ; Bulent Tutmez ; Chin-Teng Lin ; Joao M. C. Sousa ; Uzay Kaymak ; Trevor Martin. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015.

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title = "Automatic seizure detection in multichannel EEG using McCIT2FIS approach",

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.",

keywords = "complex-valued fuzzy system, electroencephalogram, interval type-2, noisy data, Seizure detection",

author = "S. Dora and B. Rangarajan and K. Subramanian and S. Suresh",

year = "2015",

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doi = "10.1109/FUZZ-IEEE.2015.7338085",

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booktitle = "FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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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, NR Pal, H Ishibuchi, B Tutmez, C-T Lin, JMC 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., IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, Istanbul, Turkey, 2/08/15. https://doi.org/10.1109/FUZZ-IEEE.2015.7338085

Automatic seizure detection in multichannel EEG using McCIT2FIS approach. / Dora, S.; Rangarajan, B.; Subramanian, K. et al.
FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. ed. / Adnan Yazici; Nikhil R. Pal; Hisao Ishibuchi; Bulent Tutmez; Chin-Teng Lin; Joao M. C. Sousa; Uzay Kaymak; Trevor Martin. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. 7338085.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic seizure detection in multichannel EEG using McCIT2FIS approach

AU - Dora, S.

AU - Rangarajan, B.

AU - Subramanian, K.

AU - Suresh, S.

PY - 2015/11/25

Y1 - 2015/11/25

N2 - 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.

AB - 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.

KW - complex-valued fuzzy system

KW - electroencephalogram

KW - interval type-2

KW - noisy data

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BT - FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems

A2 - Yazici, Adnan

A2 - Pal, Nikhil R.

A2 - Ishibuchi, Hisao

A2 - Tutmez, Bulent

A2 - Lin, Chin-Teng

A2 - Sousa, Joao M. C.

A2 - Kaymak, Uzay

A2 - Martin, Trevor

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015

Y2 - 2 August 2015 through 5 August 2015

ER -

Dora S, Rangarajan B, Subramanian K, Suresh S. Automatic seizure detection in multichannel EEG using McCIT2FIS approach. In Yazici A, Pal NR, Ishibuchi H, Tutmez B, Lin CT, Sousa JMC, Kaymak U, Martin T, editors, FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems. Vol. 2015-November. Institute of Electrical and Electronics Engineers Inc. 2015. 7338085 doi: 10.1109/FUZZ-IEEE.2015.7338085

Automatic seizure detection in multichannel EEG using McCIT2FIS approach

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Keywords

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