Automated detection of the preseizure state in EEG signal using neural networks

Biocybernetics and Biomedical Engineering - Tập 39 - Trang 160-175 - 2019
C. Sudalaimani1,2, N. Sivakumaran1, Thomas T. Elizabeth2, Valsalam S. Rominus2
1Instrumentation and Control Engineering (ICE), National Institute of Technology (NIT), Tiruchirappalli, Tamil Nadu, India
2Health and Software Technology Group (HSTG), Centre for Development of Advanced Computing (C-DAC), Thiruvananthapuram, Kerala, India

Tài liệu tham khảo

Parvez, 2014, EEG signal classification using frequency band analysis towards epileptic seizure prediction, 126 https://www.kaggle.com/c/seizure-prediction. Ali, 2014, Seizure prediction methods: a review of the current predicting techniques, Biomed Pharmacol J, 7, 153, 10.13005/bpj/466 Nilufer, 2014, Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers, Comput Math Methods Med D’Alessandro, 2003, Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients, IEEE Trans Biomed Eng, 50 Carney, 2011, Seizure prediction: methods, Epilepsy Behav, 22 Schelter, 2006, Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies, Epilepsia, 47, 2058, 10.1111/j.1528-1167.2006.00848.x Alotaiby, 2014, EEG seizure detection and prediction algorithms: a survey, EURASIP J Adv Signal Process, 2014, 183, 10.1186/1687-6180-2014-183 Brinkmann, 2016, Crowd sourcing reproducible seizure forecasting in human and canine epilepsy, Brain, 139, 1713, 10.1093/brain/aww045 Howbert, 2014, Forecasting seizures in dogs with naturally occurring epilepsy, PLoS One, 9, e81920, 10.1371/journal.pone.0081920 Park, 2011, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia, 52, 1761, 10.1111/j.1528-1167.2011.03138.x Jacobs, 2009, High frequency oscillations (80–500Hz) in the preictal period in patients with focal seizures, Epilepsia, 50, 1780, 10.1111/j.1528-1167.2009.02067.x Feldwisch-Drentrup, 2010, Joining the benefits: combining epileptic seizure prediction methods, Epilepsia, 51, 1598, 10.1111/j.1528-1167.2009.02497.x Kiral-Kornek, 2018, Epileptic seizure prediction using big data and deep learning: Toward a mobile system, EBioMedicine, 27, 103, 10.1016/j.ebiom.2017.11.032 Stacey, 2018 Ramgopal, 2014 Nagaraj, 2015, The future of seizure prediction and intervention: closing the loop, J Clin Neurophysiol, 32, 194, 10.1097/WNP.0000000000000139 Varatharajah, 2017, Seizure forecasting and the preictal state in canine epilepsy, Int J Neural Syst, 27, 1650046, 10.1142/S0129065716500465 Alotaiby, 2017, Epileptic seizure prediction using CSP and LDA for scalp EEG signals, Comput Intell Neurosci, 1, 10.1155/2017/1240323 Browne, 2000 Niedermeyer, 2004 Blanco, 2013, Comparison of frequency bands using spectral entropy for epileptic seizure prediction, ISRN Neurol, 10.1155/2013/287327 Akiyama, 2011, Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy, Epilepsia, 1 Anusha, 2012, Classification of normal and epileptic EEG signal using time & frequency domain features through artificial neural network Sun, 1997, Time-frequency analysis of high-frequency activity at the start of epileptic seizures Franaszczuk, 1999, Time-frequency analysis of EEG signal complexity during epileptic seizures Zavid Parvez, 2014, EEG signal classification using frequency band analysis towards epileptic seizure prediction Mormann, 2007, Seizure prediction: the long and winding road, Brain, 130, 314, 10.1093/brain/awl241 Andrzejak, 2009, Seizure prediction: any better than chance?, Clin Neurophysiol, 120, 1465, 10.1016/j.clinph.2009.05.019 Mormann, 2016, Seizure prediction: making mileage on the long and winding road, Brain, 139, 1625, 10.1093/brain/aww091 Cook, 2013, Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study, Lancet Neurol, 12, 563, 10.1016/S1474-4422(13)70075-9 Jeffry, 2014, Forecasting seizures in dogs with naturally occurring epilepsy, PLoS One, 9, e81920, 10.1371/journal.pone.0081920 Fujita, 2014, Preictal activity of subicular, CA1, and dentate gyrus principal neurons in the dorsal hippocampus before spontaneous seizures in a rat model of temporal lobe epilepsy, J Neurosci, 34, 16671, 10.1523/JNEUROSCI.0584-14.2014 WolfgangLöscher, 2011, Critical review of current animal models of seizures and epilepsy used in the discovery and development of new antiepileptic drugs, J Seizure, 20, 359 Sood, 2014, Sciences design and development of prediction model to detect seizure activity utilizing higher order statistical features of EEG signals, Res J Pharm Biol Chem, 5, 11 Nikias, 1993, Higher-order spectral analysis. Engineering in medicine and biology society Mirowski, 2008, Classification of patterns of EEG synchronization for seizure prediction Howbert, 2014, Forecasting seizures in dogs with naturally occurring epilepsy, PLoS One, 9, e81920, 10.1371/journal.pone.0081920 Park, 2011, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia, 52, 1761, 10.1111/j.1528-1167.2011.03138.x Patterson, 2014, Canine epilepsy: an underutilized model, ILAR J, 55, 182, 10.1093/ilar/ilu021 Leppik, 2011, Canine status epilepticus: a translational platform for human therapeutic trials, Epilepsia, 52, 31, 10.1111/j.1528-1167.2011.03231.x Tatum, 2014 Baumgartner, 1998, Preictal SPECT in temporal lobe epilepsy: regional cerebral blood flow is increased prior to electroencephalography-seizure onset, J Nucl Med, 39, 978 Zandi, 2010, Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG, 5537 Zandi, 2013, Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals, IEEE Trans Biomed Eng, 60, 1401, 10.1109/TBME.2012.2237399 Aarabi, 2012, A rule-based seizure prediction method for focal neocortical epilepsy, Clin Neurophysiol, 123, 1111, 10.1016/j.clinph.2012.01.014 Schelter, 2011, Seizure prediction in epilepsy: From circadian concepts via probabilistic forecasting to statistical evaluation, 1624 Wang, 2010, A novel reinforcement learning framework for online adaptive seizure prediction, 499 Li, 2013, Seizure prediction using spike rate of intracranial EEG, IEEE Trans Neural Syst Rehabil Eng, 21, 880, 10.1109/TNSRE.2013.2282153 Niknazar, 2016, Epileptic seizure prediction using a new similarity index for chaotic signals, Int J Bifurc Chaos, 26, 10.1142/S0218127416501868 Miri, 2011, A new seizure prediction method based on return map, 244 Rogowski, 1981, On the prediction of epileptic seizures, Biol Cybern, 42, 9, 10.1007/BF00335153 Salant, 1998, Prediction of epileptic seizures from two-channel EEG, Med Biol Eng Comput, 36, 549, 10.1007/BF02524422 Zhu, 2009, Epileptic seizure prediction by using empirical mode decomposition and complexity analysis of single-channel scalp electroencephalogram, 1 Zheng, 2014, Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition, Clin Neurophysiol, 125, 1104, 10.1016/j.clinph.2013.09.047 Williamson, 2012, Seizure prediction using EEG spatiotemporal correlation structure, Epilepsy Behav, 25, 230, 10.1016/j.yebeh.2012.07.007 Kuhlmann, 2010, Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons, Epilepsy Res, 91, 214, 10.1016/j.eplepsyres.2010.07.014 Sackellares, 2006, Predictability analysis for an automated seizure prediction algorithm, J Clin Neurophysiol, 23, 509, 10.1097/00004691-200612000-00003 Bedeeuzzaman, 2014, Seizure prediction using statistical dispersion measures of intracranial EEG, Biomed Signal Process Control, 10, 338, 10.1016/j.bspc.2012.12.001 Iasemidis, 2005, Long-term prospective on-line real-time seizure prediction, Clin Neurophysiol, 116, 532, 10.1016/j.clinph.2004.10.013 Chaovalitwongse, 2005, Performance of a seizure warning algorithm based on the dynamics of intracranial EEG, Epilepsy Res, 64, 93, 10.1016/j.eplepsyres.2005.03.009 Pardalos, 2004, Seizure warning algorithm based on optimization and nonlinear dynamics, Math Program, 101, 365, 10.1007/s10107-004-0529-4 Elger, 1998, Seizure prediction by non-linear time series analysis of brain electrical activity, Eur J Neurosci, 10, 786, 10.1046/j.1460-9568.1998.00090.x Mormann, 2005, On the predictability of epileptic seizures, Clin Neurophysiol, 116, 569, 10.1016/j.clinph.2004.08.025 Chisci, 2010, Real-time epileptic seizure prediction using AR models and support vector machines, IEEE Trans Biomed Eng, 57, 1124, 10.1109/TBME.2009.2038990 Hung, 2010, VLSI implementation for epileptic seizure prediction system based on wavelet and chaos theory, 364 Chiang, 2011, Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme, 7564 Gadhoumi, 2013, Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity, Clin Neurophysiol, 124, 1745, 10.1016/j.clinph.2013.04.006 Wang, 2013, Online seizure prediction using an adaptive learning approach, IEEE Trans Knowl Data Eng, 25, 2854, 10.1109/TKDE.2013.151 Costa, 2008, Epileptic seizure classification using neural networks with 14 features, 281 Bandarabadi, 2015, Epileptic seizure prediction using relative spectral power features, Clin Neurophysiol, 126, 237, 10.1016/j.clinph.2014.05.022 Vahabi, 2015, Online epileptic seizure prediction using wavelet-based bi-phase correlation of electrical signals tomography, Int J Neural Syst, 25, 10.1142/S0129065715500288 Myers, 2016, Seizure prediction and detection via phase and amplitude lock values, Front Hum Neurosci, 10, 10.3389/fnhum.2016.00080 Consul, 2013, Hardware efficient seizure prediction algorithm Park, 2011, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia, 52, 1761, 10.1111/j.1528-1167.2011.03138.x Moghim, 2014, Predicting epileptic seizures in advance, PLoS One, 9, 10.1371/journal.pone.0099334 Mirowski, 2009, Classification of patterns of EEG synchronization for seizure prediction, Clin Neurophysiol, 120, 1927, 10.1016/j.clinph.2009.09.002 Ghaderyan, 2014, An efficient seizure prediction method using KNN-based undersampling and linear frequency measures, J Neurosci Methods, 232, 134, 10.1016/j.jneumeth.2014.05.019 Direito, 2017, A realistic seizure prediction study based on multiclass SVM, Int J Neural Syst, 27, 10.1142/S012906571750006X Bradley Andrew, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit, 30, 1145, 10.1016/S0031-3203(96)00142-2 Ramgopal Sriram, 2014, Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy, Epilepsy Behav, 37, 291, 10.1016/j.yebeh.2014.06.023 Brinkmann Benjamin, 2015, Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy, PLoS One, 10.1371/journal.pone.0133900 Wong Kin Foon Kevin, 2006, Modelling non-stationary variance in EEG time series by state space GARCH model, Comput Biol Med, 36.12, 1327, 10.1016/j.compbiomed.2005.10.001 Schachter Steven, 1993, Advances in the assessment of refractory epilepsy, Epilepsia, 34 Park Yun, 2011, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia, 52 Berendt, 2018, Epilepsy Kshirsagar, 2016, Prediction of neurological disorders using optimized neural network Hagan, 2008, Neural network design Shivanandan, 2010 Alotaiby, 2014, EEG seizure detection and prediction algorithms: a survey, EURASIP J Adv Signal Process, 2014, 183, 10.1186/1687-6180-2014-183 Donald, 1991, A general regression neural network, IEEE Trans Neural Netw, 2 Serap AYDIN, 2010, Determination of autoregressive model orders for seizure detection, Turk J Electr Eng Comput Sci, 18 Aarabi, 2009, EEG seizure prediction: measures and challenges Dean, 2018 Jones, 1998, Comparing measures of sample skewness and kurtosis, J Roy Stat Soc Ser D Stat, 47, 183, 10.1111/1467-9884.00122 https://brownmath.com/stat/shape.htm. (Accessed on 29.03.2017, 9.00 pm). Mouhammad Usman, 2017, Epileptic seizure prediction using machine learning methods, Hindawi, Comput Math Methods Med Westfall, 2014, Kurtosis as peakedness, 1905 R.I.P., Am Stat, 68, 191, 10.1080/00031305.2014.917055 https://electronics.stackexchange.com/questions/77675/definition-of-power-signals-and-energy-signals. (Accessed on 31.03.2018, 9.43 am). Direito, 2008, Combining energy and wavelet transform for epileptic seizure prediction in an advanced computational system Rasekhi, 2015, Epileptic seizure prediction based on ratio and differential linear univariate features, J Med Signals Sens, 5, 1, 10.4103/2228-7477.150371 Padmasair, 2010, Linear prediction modelling for the analysis of the epileptic EEG Alkan, 2005, Automatic seizure detection in EEG using logistic regression and artificial neural network, J Neurosci Methods, 148, 167, 10.1016/j.jneumeth.2005.04.009 Kim, 2013, Coercively adjusted auto regression model for forecasting in epilepsy EEG, Comput Math Methods Med, 2013, 545613, 10.1155/2013/545613 Sudalaimani, 2017, Seizure prediction using general regression neural network