Abstract
Seizures caused by epilepsy are unprovoked, they disrupt the mantel activity of the patient and impair their normal motor and sensorial functions, endangering the patient’s well being. Exploiting today’s technology it is possible toe create automatic systems to monitor and evaluate patients. An area of special interest is the automatic analysis of EEG signals. This paper presents extensive analysis of feature extraction and classification methods that have reported good results in other EEG based problems. Several methods are detailed to extract 52 features from the time, frequency and time-frequency domains in order to characterize the EEG signals. Additionally, 10 different classification models, together with a feature selection method, are implemented using these features to identify if a signal corresponds to an epileptic state. The experiments were performed using the standard BONN and the proposed method achieve results comparable to those in the state-of-the-art for the three and four classes problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sotelo Arturo, Guijarro Enrique, Trujillo Leonardo, Coria Luis N, Martnez Yuliana (2013) Identification of epilepsy stages from ECoG using genetic programming classifiers. Comput Biol Med 43(11):1713–1723
Sotelo A (2015) Enrique D Guijarro, and Leonardo Trujillo. Seizure states identification in experimental epilepsy using gabor atom analysis. J Neurosci Methods 241:121–131
Flores EZ, Trujillo L, Sotelo A, Legrand P, Coria LN (2016) Regularity and matching pursuit feature extraction for the detection of epileptic seizures. J Neurosci Methods 266:107–125
Vézard L, Legrand P, Chavent M, Fata-Anseba F, Trujillo L (2015) Eeg classification for the detection of mental states. Appl. Soft Comput 32(C):113–131
Zheng W-L, Lu B-L (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev 7(3):162–175
Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from eeg. IEEE Trans Affect Comput 5(3):327–339
Fisher RS, Acevedo C, Arzimanoglou A, Bogacz A, Cross JH, Elger CE, Engel J, Forsgren L, French JA, Glynn M, Hesdorffer DC, Lee BI, Mathern GW, Moshé SL, Perucca E, Scheffer IE, Tomson T, Watanabe M, Wiebe S (2014) ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55(4):475–482
Thurman DJ, Beghi E, Begley CE, Berg AT, Buchhalter JR, Ding D, Hesdorffer DC, Hauser WA, Kazis L, Kobau R, Kroner B, Labiner D, Liow K, Logroscino G, Medina MT, Newton CR, Parko K, Paschal A, Preux P-M, Sander JW, Selassie A, Theodore W, Tomson T, Wiebe S (2011) Standards for epidemiologic studies and surveillance of epilepsy. Epilepsia 52 Suppl 7(1):2–26
Eadie MJ (2012) Shortcomings in the current treatment of epilepsy. Expert Rev Neurother 12(12):1419–1427
Franaszczuk PJ, Bergey GK, Durka PJ, Eisenberg HM (1998) Time-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe. Electroencephalogr Clin Neurophysiol 106(6):513–521
Kohsaka S, Mizukami S, Kohsaka M, Shiraishi H, Kobayashi K (2002) Widespread activation of the brainstem preceding the recruiting rhythm in human epilepsies. Neuroscience 115(3):697–706
Acharya UR, Vinitha Sree S, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev 64(6 Pt 1)
Xie S, Krishnan S (2014) Dynamic principal component analysis with nonoverlapping moving window and its applications to epileptic EEG classification. Sci World J 1:2014
Kamath C (2015) Analysis of EEG dynamics in epileptic patients and healthy subjects using Hilbert transform scatter plots. OALib 02:1–14
Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng KH, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408
Ahammad N, Fathima T, Joseph P (2014) Detection of epileptic seizure event and onset using EEG. BioMed Res Int 450573. http://dx.doi.org/10.1155/2014/450573.
Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193(1):156–163
Orhan U, Hekim M, Ozer M (2011) Eeg signals classification using the k-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38(10):13475–13481
Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans Info Technol Biomed Publ IEEE Eng Med Bio Soc 13(5):703–710
Kovacs P, Samiee K, Gabbouj M (2014) On application of rational discrete short time fourier transform in epileptic seizure classification. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings, pp 5839–5843. http://dx.doi.org/10.1109/ICASSP.2014.6854723
Bajaj V, Pachori RB (2012) EEG signal classification using empirical mode decomposition an d support vector machine. In: Proceedings of the International Conference on Soft Computing, pp 581–592
Guler N, Ubeyli E, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29(3):506–514
Durka PJ, Blinowska KJ (1995) Analysis of eeg transients by means of matching pursuit. Ann Biomed Eng 23(5):608–611
Durka PJ, Ircha D, Blinowska KJ (2001) Stochastic time-frequency dictionaries for matching pursuit. IEEE Trans Signal Process 49(3):507–510
Durka PJ, Matysiak A, Montes EM, Sosa PV, Blinowska KJ (2005) Multichannel matching pursuit and EEG inverse solutions. J Neurosci Methods 148(1):49–59
Hjorth Bo (1970) Eeg analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29(3):306–310
Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AL, Kaliton D, Goldberger AL (2000) Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J Appl Physiol 88(6):2045–2053
Petrantonakis PC, Hadjileontiadis LJ (2010) Emotion recognition from eeg using higher order crossings. IEEE Trans Inf Technol Biomed 14(2):186–197
Ackermann P, Kohlschein C, Bitsch JA, Wehrle K, Jeschke S (2016) Eeg-based automatic emotion recognition: feature extraction, selection and classification methods. In: 2016 IEEE 18th International Conference on e-health Networking, Applications and Services (Healthcom), pp 1–6
Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181
Ball T, Kern M, Mutschler I, Aertsen A, Schulze-Bonhage A (2009) Signal quality of simultaneously recorded invasive and non-invasive EEG. NeuroImage 46(3):708–716
Acknowledgements
This work was founded through the project “Clasificador de emociones 2.0 utilizando medios fisiológicos, cerebrales y conductuales” under the PEI 2017 program, with the collaboration of ITT and Neuroaplicaciones y Tecnologías S.A. de C.V.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Hernández, D.E., Trujillo, L., Z-Flores, E., Villanueva, O.M., Romo-Fewell, O. (2018). Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features. In: Sanchez, M., Aguilar, L., Castañón-Puga, M., Rodríguez-Díaz, A. (eds) Computer Science and Engineering—Theory and Applications. Studies in Systems, Decision and Control, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-319-74060-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-74060-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74059-1
Online ISBN: 978-3-319-74060-7
eBook Packages: EngineeringEngineering (R0)