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Improving classification of epileptic and non-epileptic EEG events by feature selection

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Improving classification of epileptic and non-epileptic EEG events by feature selection. / Mporas, Iosif; Pippa, Evangelia ; Zacharaki, Evangelia I.; Tsirka, Vasiliki ; Richardson, Mark P. ; Koutroumanidis, Michael ; Megalooikonomou, Vasileios .

In: Neurocomputing, Vol. 171, 01.01.2016, p. 576-585.

Research output: Contribution to journalArticlepeer-review

Harvard

Mporas, I, Pippa, E, Zacharaki, EI, Tsirka, V, Richardson, MP, Koutroumanidis, M & Megalooikonomou, V 2016, 'Improving classification of epileptic and non-epileptic EEG events by feature selection', Neurocomputing, vol. 171, pp. 576-585. https://doi.org/10.1016/j.neucom.2015.06.071

APA

Mporas, I., Pippa, E., Zacharaki, E. I., Tsirka, V., Richardson, M. P., Koutroumanidis, M., & Megalooikonomou, V. (2016). Improving classification of epileptic and non-epileptic EEG events by feature selection. Neurocomputing, 171, 576-585. https://doi.org/10.1016/j.neucom.2015.06.071

Vancouver

Mporas I, Pippa E, Zacharaki EI, Tsirka V, Richardson MP, Koutroumanidis M et al. Improving classification of epileptic and non-epileptic EEG events by feature selection. Neurocomputing. 2016 Jan 1;171:576-585. https://doi.org/10.1016/j.neucom.2015.06.071

Author

Mporas, Iosif ; Pippa, Evangelia ; Zacharaki, Evangelia I. ; Tsirka, Vasiliki ; Richardson, Mark P. ; Koutroumanidis, Michael ; Megalooikonomou, Vasileios . / Improving classification of epileptic and non-epileptic EEG events by feature selection. In: Neurocomputing. 2016 ; Vol. 171. pp. 576-585.

Bibtex

@article{5507dc51a7bd46f1ad7984b98f3a8ef2,
title = "Improving classification of epileptic and non-epileptic EEG events by feature selection",
abstract = "Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.",
author = "Iosif Mporas and Evangelia Pippa and Zacharaki, {Evangelia I.} and Vasiliki Tsirka and Richardson, {Mark P.} and Michael Koutroumanidis and Vasileios Megalooikonomou",
note = "This is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright {\textcopyright} 2015 Elsevier B.V. ",
year = "2016",
month = jan,
day = "1",
doi = "10.1016/j.neucom.2015.06.071",
language = "English",
volume = "171",
pages = "576--585",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Improving classification of epileptic and non-epileptic EEG events by feature selection

AU - Mporas, Iosif

AU - Pippa, Evangelia

AU - Zacharaki, Evangelia I.

AU - Tsirka, Vasiliki

AU - Richardson, Mark P.

AU - Koutroumanidis, Michael

AU - Megalooikonomou, Vasileios

N1 - This is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright © 2015 Elsevier B.V.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.

AB - Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.

UR - http://www.sciencedirect.com/science/article/pii/S0925231215009509

U2 - 10.1016/j.neucom.2015.06.071

DO - 10.1016/j.neucom.2015.06.071

M3 - Article

VL - 171

SP - 576

EP - 585

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -