Comparison of epileptic seizure prediction performance for different EEG derivation schemes
Keywords:epilepsy, epileptic seizure, correlation, electroencephalography, EEG, seizure prediction, derivation scheme
AbstractIntroduction. The influence of EEG derivation scheme on performance of epileptic seizure prediction is considered in the paper.
Comparison of epileptic seizure prediction performance for different EEG derivation schemes is conducted. Database of EEG recordings from 20 patients (between 1 and 25 years old) suffering epilepsy was used. Correlation coefficients between channels of signal extracted with different combination of window and preictal lengths were used as features. Support vector machine was used to classify EEG data into interictal and preictal classes. Epileptic seizures prediction performance was evaluated using area under the ROC-curve for two types of derivation schemes: monopolar scheme with reference electrode on ipsilateral ear and scheme with averaged reference.
Conclusions. It was found that there is a difference in epileptic seizures prediction performance for different schemes and patients. For subgroup of patients (9 of 20) usage of scheme with reference electrode in ipsilateral ear shows higher performance for window length between 2 and 10 seconds and in entire range of preictal lengths. Scheme with averaged reference shows higher performance when window length is in the range between 90 and 300 seconds.
Schelter B., Timmer J. and Schulze-Bonhage A. (2008) Seizure prediction in epilepsy: from basic mechanisms to clinical applications, John Wiley & Sons. DOI: 10.1002/9783527625192
Engel J. (2013) Seizures and epilepsy, Oxford University Press. DOI: 10.1093/med/9780195328547.001.0001
Schachter S.C. and Wheless J.W. (2002) Vagus Nerve Stimulation Therapy 5 Years After Approval: A Comprehensive Update. Lippincott Williams & Wilkins
Osorio I., Frei M.G., Sunderam S., Giftakis J., Bhavaraju N.C., Schaffner S.F. and Wilkinson S. B. (2005) Automated seizure abatement in humans using electrical stimulation, Annals of Neurology, Vol. 57, No. 2, pp. 258-268. DOI: 10.1002/ana.20377
Iasemidis L. D. (2003) Epileptic seizure prediction and control, IEEE Transactions on Biomedical Engineering, Vol. 50, No. 5, pp. 549-558. DOI: 10.1109/tbme.2003.810705
Mormann F., Andrzejak R.G., Elger C.E. and Lehnertz K. (2007) Seizure prediction: the long and winding road, Brain, Vol. 130, No. 2, pp. 314–333. DOI: 10.1093/brain/awl241
Carney P. R., Myers S. and Geyer J. D. (2011) Seizure prediction: methods. Epilepsy & behavior, Vol. 22, Suppl. 1., pp. S94-S101. DOI: 10.1016/j.yebeh.2011.09.001
Klem G. H., Lüders H. O., Jasper H. H. and Elger C. (1999) The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl., Vol. 52, pp. 3–6.
Zenkov L. R. (2004) Klinicheskaya elektroentsefalografiya (s elementami epileptologii) [Clinical electroencephalography (with elements of epileptology)]. Moskva, MEDpress-inform.
Teplan M. (2002) Fundamentals of EEG measurement. Measurement science review, Vol. 2, No. 2, pp. 1–11.
Panichev O., Popov A. and Kharytonov V. (2016) Patient-specific epileptic seizure prediction based on evaluation of synchronization between brain regions. Computer Methods and Programs in Biomedicine. (Submitted for publication)
Zhukov M., Popov A., Panichev O. and Kharitonov V. (2015) Correlation between EEG channels for epileptic seizure prediction. Electronics and Communications, Vol. 21, No 6, pp. 41-45(in Ukrainian)
Mirowski P., Madhavan D., LeCun Y. and Kuzniecky R. (2009) Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology, Vol. 120, No. 11, pp. 1927–1940. DOI: 10.1016/j.clinph.2009.09.002
Varsavsky A., Mareels I. and Cook M. (2010) Epileptic seizures and the EEG, CRC Press. DOI: 10.1201/b10459
Panichev O., Popov A. and Kharytonov V. (2015) Patient-specific epileptic seizure prediction using correlation features. Signal Processing Symposium (SPSympo). DOI: 10.1109/SPS.2015.7168309
Cortes C. and Vapnik V. (1995) Support-vector networks, Machine Learning, Vol. 20, No. 3, pp. 273-297. DOI: 10.1007/bf00994018
Bradley A.P. (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition. Vol. 30, No. 7, pp. 1145–1159. DOI: 10.1007/bf00994018
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