USE OF CORRELATION ANALYSIS FOR ONSET EPILEPTIC SEIZURE DETECTION

English version České info

In this paper we present our implementation of correlation analysis method used for detecting epileptic seizures during their onset zone. We used matrices of correlation coefficients and their time course to classify seizure. We also developed effective method to reduce number of features. Implemented algorithms were tested on our own dataset acquired in cooperation with the neurological department of FN Motol. The results show this approach as reasonable but the accuracy must be significantly improved before use in practice.

Keywords:
scalp EEG, onset seizure detection, epilepsy, correlation analysis, ictal SPECT

Autoři: Tomáš Havel; Jiří Balach
Působiště autorů: Faculty of Electrical Engineering, CTU in Prague, Czech Republic
Vyšlo v časopise: Lékař a technika - Clinician and Technology No. 2, 2012, 42, 92-95
Kategorie: Conference YBERC 2012

Souhrn

Keywords:
scalp EEG, onset seizure detection, epilepsy, correlation analysis, ictal SPECT

Zdroje

[1] Funkčně zobrazovací vyšetření. Brázdil, M. a Marusič, P. Epilepsie temporálního laloku. Vyd. 1. Praha: Triton, 2006, s. 172-187. ISBN 80-7254-836-0.

[2] Duun-Henriksen, J., Kjaer, T. W., Madsen, R. E., Remvig, L. S., Thomsen, C. E., Sorensen, H. B. D. Channel selection for automatic seizure detection, Clinical Neurophysiology, Volume 123, Issue 1, January 2012, Pages 84-92, ISSN 1388-2457

[3] Janča, R., Čmejla, R., Jahodová, A. Rules for Spike Detection in Multichanel Intracranial Electroencephalography 19th Annual Conference Proceedings Technical Computing Prague 2011 Vydavatelství VŠCHT Praha, 2011, pp. -⁠

[4] Schindler, K., Leung, H., Elger, C. E., & Lehnertz, K. (2007). Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. Brain: A journal of neurology, 130(Pt 1), 65-77. Oxford Univ Press. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17082199

[5] Müller, M.; Baier, G.; Galka, A.; Stephani, U. & Muhle, H. Detection and characterization of changes of the correlation structure in multivariate time series Phys. Rev. E, American Physical Society, 2005, 71, 046116, DOI: 10.1103/PhysRevE.71.046116.

[6] Gotman, J. Automatic recognition of epileptic seizures in the EEG. Electroencephalography and Clinical Neurophysiology. 1982, roč. 54, č. 5, s. 530-540. ISSN 00134694. DOI: 10.1016/0013-4694(82)90038-4.

[7] Hao Qu, Gotman, J. A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device. IEEE Transactions on Biomedical Engineering. 1997, roč. 44, č. 2, s. 115-122. ISSN 00189294. DOI: 10.1109/10.552241. Retrieved from http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? Arnumber=552241

[8] Iscan, Z., Dokur, Z., Demiralp, T. Classification of electroencephalogram signals with combined time and frequency features. Expert Systems with Applications. 2011, roč. 38, č. 8, s. 10499-10505. ISSN 09574174. DOI: 10.1016/j.eswa.2011.02.110. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0957417411003162

[9] Mcsharry, P. E., He, T., Smith, L. A., Tarassenko, L. Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings. Medical. 2002, roč. 40, č. 4, s. 447-461. ISSN 0140-0118. DOI: 10.1007/BF02345078. Retrieved from http://www.springerlink.com/index/10.1007/BF02345078