Abstract
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.
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References
Abualsaud K, Massudi M, Saleh M, Mohamed A (2015) Ensemble classifier for epileptic seizure detection for imperfect EEG data. Sci World J. doi:10.1155/2015/945689
Acharya UR, Molinari F, Vinitha SS, Chattopadhyay S, Kwan-Hoong N, Suri JS (2012a) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408. doi:10.1016/j.bspc.2011.07.007
Acharya UR, Alvin AP, Sree SV, Yanti R, Suri JS (2012b) Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 22(2):1250002
Acharya UR, Sree SV, Suri JS, Alvin AP (2012c) Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Exp system Appl 10(39):9072–9078
Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69–87. doi:10.1016/S0165-0270(02)00340-0
Alotaiby N, El-Samie EA, Alshebeili SA, Aljibreen KH, Alkhanen E (2015) Seizure detection with common spatial pattern and support vector machines. In: Proceedings of information and communication technology research, 52–155. doi:10.1109/ICTRC.2015.7156444
Andrzejak RG (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 E. doi:10.1103/PhysRevE.64.061907
Aydin S, Saraoglu HM, Kara S (2009) Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann Biomed Eng 37(12):2626–2630. doi:10.1007/s10439-009-9795-x
Bajaj V, Pachori RB (2013) Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett 3:17–21. doi:10.1007/s13534-013-0084-0
Chaurasiya RK, Jain K, Goutam S, Manisha (2015) Epileptic seizure detection using HHT and SVM. In: Proceedings of international conference electrical electronics signals communication and optimization, pp 1–6. doi:10.1109/EESCO.2015.7253660
Coifman R, Meyer Y, Quake S, Wickerhauser MV (1990) Signal processing and compression with wavelet packets. J Byres, Wavelets and their applications. Springer, Netherland, pp 363–379
Das AB, Bhuiyan MH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy based features in the EMD-DWT domain. Biomed Signal Process Control 29:11–21
Du M, Li J, Wang R (2016) The influence of potassium concentration on epileptic seizures in a coupled neuronal model in the hippocampus. Cogn Neurodyn 10(5):405–414
Elman JL (1990) Finding structure in time. Cognit Sci 14:179–211
Faust O, Acharya UR, Adeli H (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56–64
Fisher RS, Boas EW, Blume W, Elger C (2005) Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 46(4):470–472
Gajic D, Zeljko D, Stefano DG, Fredrik G (2014) Classification of EEG signals for detection of epileptic seizure based on wavelet and statistical pattern recognition. Biomed Eng Appl Basis Commun 26(2):1450021
Gao J, Hu J, Tung W (2011) Complexity measures of brain wave dynamics. Cogn Neurodyn 5(2):171–182
Gopan GK, Sinha N, Babu DJ (2015) Statistical features based epileptic seizure EEG detection—an efficacy evaluation. In: Proceedings of advances in computing, communications and informatics (ICACCI), pp 1394–1398. doi:10.1109/ICACCI.2015.7275808
Gotman J (1982) Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol 99:530–540
Gotman J, Deng L (1991) State-dependent spike detection: concepts and preliminary results. Electroencephalogr Clin Neurophysiol 70:11–19
Guo L, Riveero D, Pazos A (2010) Epileptic seizure detection using multi wavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193:156–163. doi:10.1016/j.jneumeth.2010.08.030
Han CX, Deng J, Yi GS, Che YQ (2013) Investigation of EEG abnormalities in the early stage of Parkinson’s disease. Cogn Neurodyn 7(4):351–359. doi:10.1007/s11571-013-9247-z
Haykin SS (1996) Adaptive filter theory, 3rd edn. Upper Saddle River, Prentic Hall
Kelly KM, Shiau DS, Kern RT et al (2010) Assessment of a scalp EEG-based automated seizure detection system. Clin Neurophysiol 121(11):1832–1843. doi:10.1016/j.clinph.2010.04.016
Koren J, Herta J, Draschtak S (2015) Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges. Epilepsy Behav 49:286–289. doi:10.1016/j.yebeh.2015.04.044
Kumar K, Dewal ML, Anand RS (2014) Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. SIViP 8:1323–1334. doi:10.1007/s11760-012-0362-9
Kumar TS, Kanhangad V, Pachori RB (2015) Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed Sig Proc Control 15:33–40. doi:10.1016/j.bspc.2014.08.014
Mallat S (1989) A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Pattern Anal Mach Intell 11(7):674–693. doi:10.1109/34.192463
Natwong B, Sooraksa P, Pintavirooj C, Bunluechokchai S, Ussawawongaraya W (2006) Wavelet entropy analysis of the high resolution ECG. In: Proceedings of IEEE industrial electronics and applications, Singapore, pp 1–4
Ocak H (2009) Automatic detection of epileptic seizure in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036. doi:10.1016/j.eswa.2007.12.065
Panda R, Khobragade PS, Jambhule PD, Jengthe S, Pal PR, Gandhi TK (2010) Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure detection. In: Proceedings of systems in medicine and biology (ICSMB), 405–408. doi:10.1109/ICSMB.2010.5735413
Pippa E, Zacharaki IE, Mporas I, Vasiliki T et al (2016) Improving classification of epileptic and non-epileptic EEG events by feature selection. Neurocomputing 171:576–585. doi:10.1016/j.neucom.2015.06.071
Pravin SK, Sriraam N, Benakop PG, **aga BC (2010) Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 37:3284–3291. doi:10.1016/j.eswa.2009.09.051
Raghu S, Sriraam N, Pradeep KG (2015) Effect of wavelet packet log energy entropy on electroencephalogram (EEG) signals. Int J Biomed Clin Eng 4(1):32–43
Samiee K, Kovacs P, Gabbouj M (2015) Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans Biomed Eng 62(2):541–552. doi:10.1109/TBME.2014.2360101
Selik M, Baraniuk R, Blair A (2001) Signal energy versus signal power. Openstack-CNX Module: m10055, http://cnx.rice.edu/content/m10055/2.4
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423
Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288–295. doi:10.1109/TITB.2006.884369
Sriraam N (2012) EEG based automated detection of auditory loss: a pilot study. Expert Syst Appl 39(1):723–731. doi:10.1016/j.eswa.2011.07.064
Sriraam N (2013) EEG based thought translator: a BCI model for paraplegic patients. Int J Biomed Clin Eng 2(1):50–62
Sriraam N, Eswaran C (2008) An adaptive error modeling scheme for the lossless compression of EEG signals. IEEE Trans Inf Technol Biomed 12(5):587–594
Sriraam N, Shyamsunder R (2011) 3-D medical image compression using 3-D wavelet coders. Digit Signal Proc 21:100–109
Tan L, Jiang J (2008) Digital signal processing, fundamentals and applications, 2nd edn. Academic Press, New York
Tang Z, Li R (2011) An improved neural network model and its applications. J Inf Comput Sci 8(10):1881–1888
Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 2007:80510. doi:10.1155/2007/80510
Venkataraman V, Vlachos I, Faith A, Krishnan B (2014) Brain dynamics based automated epileptic seizure detection. doi:10.1109/EMBC.2014.6943748
Wang C, Zou J, Zhang J (2010) Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn. Cognit Neurodyn 4(3):233–240
Wang D, Miao D, **e C (2011) Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst Appl 38(11):14314–14320. doi:10.1016/j.eswa.2011.05.096
Wang S, Chaovalitwongse WA, Wong S (2013) Online seizure prediction using an adaptive learning approach. IEEE Trans Knowl Data Eng 25(12):2854–2866. doi:10.1109/TKDE.2013.151
**ang J, Ci L, Li H, Cao R, Wang B, Han X, Chen J (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci 243:18–25. doi:10.1016/j.jneumeth.2015.01.015
Yang BH, Yan GZ, Yan RG, Wu T (2006) Feature extraction of EEG-based brain computer interface by wavelet packet best basis decomposition. J Neural Eng 3(4):251–256
Zeng K, Jiaqing Y, Yinghua W (2016) Automatic detection of absence seizures with compressive sensing EEG. Neurocomputing 171:497–502. doi:10.1016/j.neucom.2015.06.076
Zhou W, Liu Y, Yuan Q, Li X (2013) Epileptic seizure detection using lacunarity and Bayesian linear discriminant analysis in intracranial EEG. IEEE Trans Biomed Eng 60(12):3375–3381. doi:10.1109/TBME.2013.2254486
Acknowledgments
The authors would like to acknowledge Dr. R.G. Andrzejak of University of Bonn, Germany, for providing permission to use the EEG data available in the public domain. The authors would like to thank Dr. A.S. Hegde, Centre for Neuro Science, M.S. Ramaiah Memorial Hospital, Bangalore, India for the useful discussion. The authors would also like to thank the anonymous reviewers for their helpful comments and suggestions that greatly improved the quality and clarity of the manuscript.
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Raghu, S., Sriraam, N. & Kumar, G.P. Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 11, 51–66 (2017). https://doi.org/10.1007/s11571-016-9408-y
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DOI: https://doi.org/10.1007/s11571-016-9408-y