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Long-term epileptic EEG classification via 2D mapping and textural features

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Long-term epileptic EEG classification via 2D mapping and textural features. / Samiee, Kaveh; Kiranyaz, Serkan; Gabbouj, Moncef; Saramäki, Tapio.

In: Expert Systems with Applications, Vol. 42, No. 20, 08.06.2015, p. 7175-7185.

Research output: Contribution to journalArticleScientificpeer-review

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Samiee, K, Kiranyaz, S, Gabbouj, M & Saramäki, T 2015, 'Long-term epileptic EEG classification via 2D mapping and textural features', Expert Systems with Applications, vol. 42, no. 20, pp. 7175-7185. https://doi.org/10.1016/j.eswa.2015.05.002

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Samiee, Kaveh ; Kiranyaz, Serkan ; Gabbouj, Moncef ; Saramäki, Tapio. / Long-term epileptic EEG classification via 2D mapping and textural features. In: Expert Systems with Applications. 2015 ; Vol. 42, No. 20. pp. 7175-7185.

Bibtex - Download

@article{821214672ae0472b8140066400e78802,
title = "Long-term epileptic EEG classification via 2D mapping and textural features",
abstract = "Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25{\%}, the overall sensitivity of 70.19{\%} and specificity of 97.74{\%} are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.",
keywords = "CHB-MIT dataset, Electroencephalography, Epileptic seizure classification, Haralick, Stochastic gradient descent, Textural features",
author = "Kaveh Samiee and Serkan Kiranyaz and Moncef Gabbouj and Tapio Saram{\"a}ki",
year = "2015",
month = "6",
day = "8",
doi = "10.1016/j.eswa.2015.05.002",
language = "English",
volume = "42",
pages = "7175--7185",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier",
number = "20",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Long-term epileptic EEG classification via 2D mapping and textural features

AU - Samiee, Kaveh

AU - Kiranyaz, Serkan

AU - Gabbouj, Moncef

AU - Saramäki, Tapio

PY - 2015/6/8

Y1 - 2015/6/8

N2 - Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.

AB - Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.

KW - CHB-MIT dataset

KW - Electroencephalography

KW - Epileptic seizure classification

KW - Haralick

KW - Stochastic gradient descent

KW - Textural features

U2 - 10.1016/j.eswa.2015.05.002

DO - 10.1016/j.eswa.2015.05.002

M3 - Article

VL - 42

SP - 7175

EP - 7185

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 20

ER -