Abstract
Background: Millions of neurons make up the human brain, and they play an important role in controlling the body's response to internal and external motor and sensory stimuli. These neurons can function as contact conduits between the human body and the brain. Analyzing brain signals or photographs will help one better understand cognitive function. These states are linked to a particular signal frequency that aids in the comprehension of how a complex brain system works.
Objective: Electroencephalography (EEG) is a useful method for locating brain waves associated with different countries on the scalp. Epilepsy is a condition where the brain or some part of it is overactive and sends too many signals. This results in seizures causing muscles to twitch or wholebody convulsions.
Methods: In this paper, the author has designed a model to predict epilepsy using machine learning algorithms and deep learning models. For the machine learning algorithm, different features were extracted and a particle swarm optimization algorithm was used to select the best feature which was classified using wavelet transform.Vgg16, Vgg19, and Inception V3 models are used for the detection of epilepsy.
Results: The inception V3 model results in 97.87% accuracy which is better than all other techniques. 5.1% accuracy improvement has been observed using a machine learning algorithm. The model is compared using existing work and it has been observed that the proposed model results better.
Conclusion: The technique for modeling EEG signals and insight brain signals recorded during surgical procedures has been identified in detail. 0.7% and 0.13% accuracy improvement were achieved when the model is validated on Kaggle and CHB-MIT datasets respectively.
Graphical Abstract
[1]
E.H. Reynolds, "The ILAE/IBE/WHO global campaign against epilepsy: Bringing epilepsy “out of the shadows”", Epilepsy Behav., vol. 1, no. 4, pp. S3-S8, 2000.
[http://dx.doi.org/10.1006/ebeh.2000.0104] [PMID: 12609455]
[http://dx.doi.org/10.1006/ebeh.2000.0104] [PMID: 12609455]
[3]
D. Naro, C. Rummel, K. Schindler, and R.G. Andrzejak, "Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score", Phys. Rev. E Stat. Nonlin. Soft Matter Phys., vol. 90, no. 3, p. 032913, 2014.
[http://dx.doi.org/10.1103/PhysRevE.90.032913] [PMID: 25314510]
[http://dx.doi.org/10.1103/PhysRevE.90.032913] [PMID: 25314510]
[4]
C. Geier, S. Bialonski, C.E. Elger, and K. Lehnertz, "How important is the seizure onset zone for seizure dynamics?", Seizure, vol. 25, pp. 160-166, 2015.
[http://dx.doi.org/10.1016/j.seizure.2014.10.013] [PMID: 25468511]
[http://dx.doi.org/10.1016/j.seizure.2014.10.013] [PMID: 25468511]
[5]
K.A. González Otárula, Y. Mikhaeil-Demo, E.M. Bachman, P. Balaguera, and S. Schuele, "Automated seizure detection accuracy for ambulatory EEG recordings", Neurology, vol. 92, no. 14, pp. e1540-e1546, 2019.
[http://dx.doi.org/10.1212/WNL.0000000000007237] [PMID: 30842291]
[http://dx.doi.org/10.1212/WNL.0000000000007237] [PMID: 30842291]
[6]
M.C. Casdagli, L.D. Iasemidis, R.S. Savit, R.L. Gilmore, S.N. Roper, and J. Chris Sackellares, "Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy", Electroencephalogr. Clin. Neurophysiol., vol. 102, no. 2, pp. 98-105, 1997.
[http://dx.doi.org/10.1016/S0921-884X(96)95195-4] [PMID: 9060860]
[http://dx.doi.org/10.1016/S0921-884X(96)95195-4] [PMID: 9060860]
[7]
E. Tamilia, E.H. Park, S. Percivati, J. Bolton, F. Taffoni, J.M. Peters, P.E. Grant, P.L. Pearl, J.R. Madsen, and C. Papadelis, "Surgical resection of ripple onset predicts outcome in pediatric epilepsy", Ann. Neurol., vol. 84, no. 3, pp. 331-346, 2018.
[http://dx.doi.org/10.1002/ana.25295] [PMID: 30022519]
[http://dx.doi.org/10.1002/ana.25295] [PMID: 30022519]
[8]
M.E. Drake, H. Padamadan, and S.A. Newell, "Interictal quantitative EEG in epilepsy", Seizure, vol. 7, no. 1, pp. 39-42, 1998.
[http://dx.doi.org/10.1016/S1059-1311(98)90006-1] [PMID: 9548224]
[http://dx.doi.org/10.1016/S1059-1311(98)90006-1] [PMID: 9548224]
[9]
M.R. Numer, "Frequency analysis and topographic mapping of EEG and evoked potentials in epilepsy", Electroencephalogr. Clin. Neurophysiol., vol. 69, no. 2, pp. 118-126, 1988.
[http://dx.doi.org/10.1016/0013-4694(88)90207-6] [PMID: 2446831]
[http://dx.doi.org/10.1016/0013-4694(88)90207-6] [PMID: 2446831]
[10]
D. Panet-Raymond, and J. Gotman, "Asymmetry in delta activity in patients with focal epilepsy", Electroencephalogr. Clin. Neurophysiol., vol. 75, no. 6, pp. 474-481, 1990.
[http://dx.doi.org/10.1016/0013-4694(90)90134-6] [PMID: 1693892]
[http://dx.doi.org/10.1016/0013-4694(90)90134-6] [PMID: 1693892]
[11]
M.G. Marciani, F. Stefanini, N. Stefani, M.C.E. Maschio, G.L. Gigli, S. Roncacci, C. Caltagirone, and G. Bernardi, "Lateralization of the epileptogenic focus by computerized EEG study and neuropsychological evaluation", Int. J. Neurosci., vol. 66, no. 1-2, pp. 53-60, 1992.
[http://dx.doi.org/10.3109/00207459208999789] [PMID: 1304570]
[http://dx.doi.org/10.3109/00207459208999789] [PMID: 1304570]
[12]
J. Wang, and H.G. Wieser, "Regional “rigidity” of background EEG activity in the epileptogenic zone", Epilepsia, vol. 35, no. 3, pp. 495-504, 1994.
[http://dx.doi.org/10.1111/j.1528-1157.1994.tb02468.x] [PMID: 8026394]
[http://dx.doi.org/10.1111/j.1528-1157.1994.tb02468.x] [PMID: 8026394]
[13]
C.P. Warren, S. Hu, M. Stead, B.H. Brinkmann, M.R. Bower, and G.A. Worrell, "Synchrony in normal and focal epileptic brain: The seizure onset zone is functionally disconnected", J. Neurophysiol., vol. 104, no. 6, pp. 3530-3539, 2010.
[http://dx.doi.org/10.1152/jn.00368.2010] [PMID: 20926610]
[http://dx.doi.org/10.1152/jn.00368.2010] [PMID: 20926610]
[14]
H. Osterhage, F. Mormann, M. Staniek, and K. Lehnertz, "Measuring synchronization in the epileptic brain: A comparison of different approaches", Int. J. Bifurcat. Chaos, vol. 17, no. 10, pp. 3539-3544, 2007.
[http://dx.doi.org/10.1142/S0218127407019330]
[http://dx.doi.org/10.1142/S0218127407019330]
[15]
G. Bettus, F. Wendling, M. Guye, L. Valton, J. Régis, P. Chauvel, and F. Bartolomei, "Enhanced EEG functional connectivity in mesial temporal lobe epilepsy", Epilepsy Res., vol. 81, no. 1, pp. 58-68, 2008.
[http://dx.doi.org/10.1016/j.eplepsyres.2008.04.020] [PMID: 18547787]
[http://dx.doi.org/10.1016/j.eplepsyres.2008.04.020] [PMID: 18547787]
[16]
V.L. Towle, I. Syed, C. Berger, R. Grzesczcuk, J. Milton, R.K. Erickson, P. Cogen, E. Berkson, and J.P. Spire, "Identification of the sensory/motor area and pathologic regions using ECoG coherence", Electroencephalogr. Clin. Neurophysiol., vol. 106, no. 1, pp. 30-39, 1998.
[http://dx.doi.org/10.1016/S0013-4694(97)00082-5] [PMID: 9680162]
[http://dx.doi.org/10.1016/S0013-4694(97)00082-5] [PMID: 9680162]
[17]
H.P. Zaveri, S.M. Pincus, I.I. Goncharova, R.B. Duckrow, D.D. Spencer, and S.S. Spencer, "Localization-related epilepsy exhibits significant connectivity away from the seizure-onset area", Neuroreport, vol. 20, no. 9, pp. 891-895, 2009.
[http://dx.doi.org/10.1097/WNR.0b013e32832c78e0] [PMID: 19424095]
[http://dx.doi.org/10.1097/WNR.0b013e32832c78e0] [PMID: 19424095]
[18]
R.B. Pachori, "Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition", Res. Lett. Signal Process., vol. 2008, pp. 1-5, 2008.
[http://dx.doi.org/10.1155/2008/293056]
[http://dx.doi.org/10.1155/2008/293056]
[19]
R.B. Pachori, and V. Bajaj, "Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition", Comput. Methods Programs Biomed., vol. 104, no. 3, pp. 373-381, 2011.
[http://dx.doi.org/10.1016/j.cmpb.2011.03.009] [PMID: 21529981]
[http://dx.doi.org/10.1016/j.cmpb.2011.03.009] [PMID: 21529981]
[20]
I.W. Selesnick, R.G. Baraniuk, and N.C. Kingsbury, "The dual-tree complex wavelet transform", IEEE Signal Process. Mag., vol. 22, no. 6, pp. 123-151, 2005.
[http://dx.doi.org/10.1109/MSP.2005.1550194]
[http://dx.doi.org/10.1109/MSP.2005.1550194]
[21]
O. Faust, U.R. Acharya, H. Adeli, and A. Adeli, "Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis", Seizure, vol. 26, pp. 56-64, 2015.
[http://dx.doi.org/10.1016/j.seizure.2015.01.012] [PMID: 25799903]
[http://dx.doi.org/10.1016/j.seizure.2015.01.012] [PMID: 25799903]
[22]
M. Zhou, C. Tian, R. Cao, B. Wang, Y. Niu, T. Hu, H. Guo, and J. Xiang, "Epileptic seizure detection based on EEG signals and CNN", Front. Neuroinform., vol. 12, p. 95, 2018.
[http://dx.doi.org/10.3389/fninf.2018.00095] [PMID: 30618700]
[http://dx.doi.org/10.3389/fninf.2018.00095] [PMID: 30618700]
[23]
A.S.M. Murugavel, and S. Ramakrishnan, "Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification", Med. Biol. Eng. Comput., vol. 54, no. 1, pp. 149-161, 2016.
[http://dx.doi.org/10.1007/s11517-015-1351-2] [PMID: 26296799]
[http://dx.doi.org/10.1007/s11517-015-1351-2] [PMID: 26296799]
[24]
P. Swami, T.K. Gandhi, B.K. Panigrahi, M. Tripathi, and S. Anand, "A novel robust diagnostic model to detect seizures in electroencephalography", Expert Syst. Appl., vol. 56, pp. 116-130, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.02.040]
[http://dx.doi.org/10.1016/j.eswa.2016.02.040]
[25]
P. Swami, A.K. Godiyal, and J. Santhosh, "Robust expert system design for automated detection of epileptic seizures using SVM classifier", Proceedings of 2014 International Conference on Parallel, Distributed and Grid Computing Solan, India, pp. 219-222, 2014.
[26]
P. Fergus, D. Hignett, and A.J. Hussain, "An advanced machine learning approach to generalised epileptic seizure detection", Proceedings of the 10th International Conference on Intelligent Computing Taiyuan, China, pp. 112-118, 2014.
[27]
H.R.A. Ghayab, Y. Li, S. Abdulla, M. Diykh, and X. Wan, "Classification of epileptic EEG signals based on simple random sampling and sequential feature selection", Brain Inform., vol. 3, no. 2, pp. 85-91, 2016.
[http://dx.doi.org/10.1007/s40708-016-0039-1] [PMID: 27747606]
[http://dx.doi.org/10.1007/s40708-016-0039-1] [PMID: 27747606]
[28]
M.N. Tibdewal, H.R. Dey, M. Mahadevappa, A. Ray, and M. Malokar, "Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG", Biomed. Signal Process. Control, vol. 38, pp. 158-167, 2017.
[http://dx.doi.org/10.1016/j.bspc.2017.05.002]
[http://dx.doi.org/10.1016/j.bspc.2017.05.002]
[29]
R.R. Sharma, P. Varshney, R.B. Pachori, and S.K. Vishvakarma, "Automated system for epileptic EEG detection using iterative filtering", IEEE Sens. Lett., vol. 2, no. 4, pp. 1-4, 2018.
[http://dx.doi.org/10.1109/LSENS.2018.2882622]
[http://dx.doi.org/10.1109/LSENS.2018.2882622]
[30]
S.K. Prabhakar, and H. Rajaguru, Classifier with dimensionality reduction techniques for Epilepsy Classification from EEG.", In:Precision Medicine Powered by pHealth and Connected Health., Singapore:: Springer, 2018, pp. 185-189.
[http://dx.doi.org/10.1007/978-981-10-7419-6_31]
[http://dx.doi.org/10.1007/978-981-10-7419-6_31]
[31]
Y. Li, Y. Liu, W.G. Cui, Y.Z. Guo, H. Huang, and Z.Y. Hu, "Epileptic seizure detection in EEG signals using a unified temporal-spectral squeezeand-excitation network", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 4, pp. 782-794, 2020.
[http://dx.doi.org/10.1109/TNSRE.2020.2973434] [PMID: 32078551]
[http://dx.doi.org/10.1109/TNSRE.2020.2973434] [PMID: 32078551]
[32]
J.H. Seo, I. Tsuda, Y.J. Lee, A. Ikeda, M. Matsuhashi, R. Matsumoto, T. Kikuchi, and H. Kang, "Pattern recognition in epileptic EEG signals via dynamic mode decomposition", Mathematics, vol. 8, no. 4, p. 481, 2020.
[http://dx.doi.org/10.3390/math8040481]
[http://dx.doi.org/10.3390/math8040481]
[33]
R. Buettner, J. Frick, and T. Rieg, "High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands", Fortieth International Conference on Information Systems. Munich, Germany, pp. 1-16, 2019.
[34]
M. Li, W. Chen, and T. Zhang, "Automatic epileptic EEG detection using DT-CWT-based non-linear features", Biomed. Signal Process. Control, vol. 34, pp. 114-125, 2017.
[http://dx.doi.org/10.1016/j.bspc.2017.01.010]
[http://dx.doi.org/10.1016/j.bspc.2017.01.010]
[35]
C. Rummel, M. Müller, G. Baier, F. Amor, and K. Schindler, "Analyzing spatio-temporal patterns of genuine cross-correlations", J. Neurosci. Methods, vol. 191, no. 1, pp. 94-100, 2010.
[http://dx.doi.org/10.1016/j.jneumeth.2010.05.022] [PMID: 20566351]
[http://dx.doi.org/10.1016/j.jneumeth.2010.05.022] [PMID: 20566351]
[36]
S.B. Tomlinson, B.E. Porter, and E.D. Marsh, "Interictal network synchrony and local heterogeneity predict epilepsy surgery outcome among pediatric patients", Epilepsia, vol. 58, no. 3, pp. 402-411, 2017.
[http://dx.doi.org/10.1111/epi.13657] [PMID: 28166392]
[http://dx.doi.org/10.1111/epi.13657] [PMID: 28166392]
[37]
A.R. Antony, A.V. Alexopoulos, J.A. González-Martínez, J.C. Mosher, L. Jehi, R.C. Burgess, N.K. So, and R.F. Galán, "Functional connectivity estimated from intracranial EEG predicts surgical outcome in intractable temporal lobe epilepsy", PLoS One, vol. 8, no. 10, p. e77916, 2013.
[http://dx.doi.org/10.1371/journal.pone.0077916] [PMID: 24205027]
[http://dx.doi.org/10.1371/journal.pone.0077916] [PMID: 24205027]
[38]
E. Ben-Jacob, S. Boccaletti, A. Pomyalov, I. Procaccia, and V.L. Towle, "Detecting and localizing the foci in human epileptic seizures", Chaos, vol. 17, no. 4, p. 043113, 2007.
[http://dx.doi.org/10.1063/1.2805658] [PMID: 18163777]
[http://dx.doi.org/10.1063/1.2805658] [PMID: 18163777]
[39]
E.H. Park, and J.R. Madsen, "Granger causality analysis of interictaliEEG predicts seizure focus and ultimate resection", Neurosurgery, vol. 82, no. 1, pp. 99-109, 2018.
[http://dx.doi.org/10.1093/neuros/nyx195] [PMID: 28472428]
[http://dx.doi.org/10.1093/neuros/nyx195] [PMID: 28472428]
[40]
S. Lagarde, N. Roehri, I. Lambert, A. Trebuchon, A. McGonigal, R. Carron, D. Scavarda, M. Milh, F. Pizzo, B. Colombet, B. Giusiano, S. Medina Villalon, M. Guye, C.G. Bénar, and F. Bartolomei, "Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies", Brain, vol. 141, no. 10, pp. 2966-2980, 2018.
[http://dx.doi.org/10.1093/brain/awy214] [PMID: 30107499]
[http://dx.doi.org/10.1093/brain/awy214] [PMID: 30107499]
[41]
R.G. Andrzejak, F. Mormann, G. Widman, T. Kreuz, C.E. Elger, and K. Lehnertz, "Improved spatial characterization of the epileptic brain by focusing on nonlinearity", Epilepsy Res., vol. 69, no. 1, pp. 30-44, 2006.
[http://dx.doi.org/10.1016/j.eplepsyres.2005.12.004] [PMID: 16503398]
[http://dx.doi.org/10.1016/j.eplepsyres.2005.12.004] [PMID: 16503398]
[42]
R.G. Andrzejak, G. Widman, K. Lehnertz, C. Rieke, P. David, and C.E. Elger, "The epileptic process as nonlinear deterministic dynamics in a stochastic environment: An evaluation on mesial temporal lobe epilepsy", Epilepsy Res., vol. 44, no. 2-3, pp. 129-140, 2001.
[http://dx.doi.org/10.1016/S0920-1211(01)00195-4] [PMID: 11325569]
[http://dx.doi.org/10.1016/S0920-1211(01)00195-4] [PMID: 11325569]
[43]
R.G. Andrzejak, K. Schindler, and C. Rummel, "Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients", Phys. Rev. E Stat. Nonlin. Soft Matter Phys., vol. 86, no. 4, p. 046206, 2012.
[http://dx.doi.org/10.1103/PhysRevE.86.046206] [PMID: 23214662]
[http://dx.doi.org/10.1103/PhysRevE.86.046206] [PMID: 23214662]
[44]
R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C.E. Elger, "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 Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics, vol. 64, no. 6, p. 061907-, 061907-061908, 2001.
[http://dx.doi.org/10.1103/PhysRevE.64.061907] [PMID: 11736210]
[http://dx.doi.org/10.1103/PhysRevE.64.061907] [PMID: 11736210]
[45]
E. Abdulhay, M. Alafeef, A. Abdelhay, and A. Al-Bashir, "Classification of Normal, Ictal and Interictal EEG via Direct quadrature and random forest tree", J. Med. Biol. Eng., vol. 37, no. 6, pp. 843-857, 2017.
[http://dx.doi.org/10.1007/s40846-017-0239-z] [PMID: 29541014]
[http://dx.doi.org/10.1007/s40846-017-0239-z] [PMID: 29541014]
[46]
Ayodeji Olalekan Salau, and Shruti Jain, "Feature Extraction: A Survey of the Types, Techniques and Applications", 5th International Conference on Signal Processing and Communication (ICSC-2019), Jaypee Institute of Information Technology, Noida, India, pp. 158-164, 2019.
[47]
J.G. Vargas-Rubio, and B. Santhanam, "On the multiangle centered discrete fractional Fourier transform", IEEE Signal Process. Lett., vol. 12, no. 4, pp. 273-276, 2005.
[http://dx.doi.org/10.1109/LSP.2005.843762]
[http://dx.doi.org/10.1109/LSP.2005.843762]
[48]
S. Jain, and S. Paul, Recent Trends in Image and Signal Processing in Computer Vision.. Switzerland AG: Springer Nature,, 2020 [http://dx.doi.org/10.1007/978-981-15-2740-1]
[49]
N. Prashar, M. Sood, and S. Jain, "A novel cardiac arrhythmia processing using machine learning techniques", Int. J. Image Graph., vol. 20, no. 3, p. 2050023, 2020.
[http://dx.doi.org/10.1142/S0219467820500230]
[http://dx.doi.org/10.1142/S0219467820500230]
[50]
C. Bhardwaj, S. Jain, and M. Sood, "Transfer learning based robust automatic detection system for diabetic retinopathy grading", Neural Comput. Appl., vol. 33, no. 20, pp. 13999-14019, 2021.
[http://dx.doi.org/10.1007/s00521-021-06042-2]
[http://dx.doi.org/10.1007/s00521-021-06042-2]
[51]
C. Bhardwaj, S. Jain, and M. Sood, "Deep learning based diabetic retinopathy severity grading system employing quadrant ensemble model", J. Digit. Imaging, vol. 34, no. 2, pp. 440-457, 2021.
[http://dx.doi.org/10.1007/s10278-021-00418-5]
[http://dx.doi.org/10.1007/s10278-021-00418-5]
[52]
C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, and Z. Xie, "Deep learning and its applications in biomedicine", Genomics Proteomics Bioinformatics, vol. 16, no. 1, pp. 17-32, 2018.
[http://dx.doi.org/10.1016/j.gpb.2017.07.003] [PMID: 29522900]
[http://dx.doi.org/10.1016/j.gpb.2017.07.003] [PMID: 29522900]
[53]
S. Jain, "Computer-aided detection system for the classification of non-small cell lung lesions using SVM", Curr. Computeraided Drug Des., vol. 16, no. 6, pp. 833-840, 2021.
[http://dx.doi.org/10.2174/1573409916666200102122021] [PMID: 31899680]
[http://dx.doi.org/10.2174/1573409916666200102122021] [PMID: 31899680]
