Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction

Biomedical Signal Processing and Control - Tập 39 - Trang 94-102 - 2018
Emina Aličković1, Jasmin Kevrić2, Abdülhamit Subaşı3
1Linkoping University, Department of Electrical Engineering, Linkoping, 58183, Sweden
2International Burch University, Faculty of Engineering and Information Technologies, Francuske Revolucije bb. Ilidza, Sarajevo, 71000, Bosnia and Herzegovina
3Effat University, College of Engineering, Jeddah 21478, Saudi Arabia

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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

Yuan, 2012, Epileptic seizure detection with linear and nonlinear features, Epilepsy Behav., 24, 415, 10.1016/j.yebeh.2012.05.009

Aarabi, 2009, A fuzzy rule-based system for epileptic seizure detection in intracranial EEG, Clin. Neurophysiol., 120, 1648, 10.1016/j.clinph.2009.07.002

Mirowski, 2009, Classification of patterns of EEG synchronization for seizure prediction, Clin. Neurophysiol., 120, 1927, 10.1016/j.clinph.2009.09.002

Chisci, 2010, Real-time epileptic seizure prediction using AR models and support vector machines, IEEE Trans. Biomed. Eng., 57, 10.1109/TBME.2009.2038990

Park, 2011, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia, 1761, 10.1111/j.1528-1167.2011.03138.x

Mormann, 2007, Seizure prediction: the long and winding road, Brain, 130, 314, 10.1093/brain/awl241

Costa, 2008, Epileptic seizure classification using neural networks with 14 features, 281

Tafreshi, 2008, Empirical mode decomposition in epileptic seizure prediction, IEEE International Symposium on Signal Processing and Information Technology

Aarabi, 2012, A rule-based seizure prediction method for focal neocortical epilepsy, Clin. Neurophysiol., 1111, 10.1016/j.clinph.2012.01.014

Parvez, 2014, Detection of pre-stage of epileptic seizure by exploiting temporal correlation of EMD decomposed EEG signals, J. Med. Bioeng., 4, 110

CHB-MIT Scalp EEG Database, [Online]. Available: http://physionet.org/physiobank/database/chbmit/.

Shoeb, 2010, Application of machine learning to epileptic seizure detection

Khan, 2012, Latency study of seizure detection, Adv. Comput. Sci., Eng. & Appl., AISC, 166, 129

Nasehi, 2013, Patient-Specific epileptic seizure onset detection algorithm based on spectral features and IPSONN classifier, 2013 International Conference on Communication Systems and Network Technologies, 10.1109/CSNT.2013.48

Rafiuddin, 2011, Feature extraction and classification of EEG for automatic seizure detection, International Conference on Multimedia, Signal Processing and Communication Technologies, 10.1109/MSPCT.2011.6150470

Fergus, 2014, An advanced machine learning approach to generalised epileptic seizure detection, Intell. Comput. Bioinf., LNBI, 8590, 112

Kevric, 2014, The effect of multiscale PCA de-noising in epileptic seizure detection, J. Med. Syst., 38, 10.1007/s10916-014-0131-0

Raghunathan, 2011, Multistage seizure detection techniques optimized for low-power hardware platforms, Epilepsy Behav., 22, S61, 10.1016/j.yebeh.2011.09.008

Majumdar, 2011, Automatic seizure detection in ECoG by differential operator and windowed variance, IEEE Trans. Neural Syst. Rehabil. Eng., 19, 356, 10.1109/TNSRE.2011.2157525

Liu, 2012, Automatic seizure detection using wavelet transform and SVM in long-Term intracranial EEG, IEEE Trans. Neural Syst. Rehabil. Eng., 20, 749, 10.1109/TNSRE.2012.2206054

Alickovic, 2015, Effect of Multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases, Circuits, Syst. Signal Process., 34, 513, 10.1007/s00034-014-9864-8

Alickovic, 2016, Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier, J. Med. Syst., 40, 1

Alickovic, 2015, The effect of denoising on classification of ECG signals, XXV International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo

Gokgoz, 2014, Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders, J. Med. Syst., 38, 1, 10.1007/s10916-014-0031-3

Uni-Freiburg, Seizure Prediction Project Freiburg, 2011. [Online]. Available: https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database. [Accessed 2 October 2011].

Shoeb, 2009

Goldberger, 2000, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, 101, 215

Bakshi, 1998, Multiscale PCA with application to multivariate statistical process monitoring, AIChE J., 44, 1596, 10.1002/aic.690440712

Kevric, 2017, Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system, Biomed. Signal Process. Control, 31, 398, 10.1016/j.bspc.2016.09.007

Huang, 1998, The empirical mode decomposition and the Hilbert spectrum fornonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A, 454, 903, 10.1098/rspa.1998.0193

Rilling, 2003, On empirical mode decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP-03) Grado

Ghorbanian, 2012, Wavelet transform EEG features of alzheimer’s disease in activated states

Unser, 1996, A review of wavelets in biomedical applications, Proceedings of the IEEE, 626, 10.1109/5.488704

Daubechies, 1990, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inf. Theory, 961, 10.1109/18.57199

Learned, 1995, A wavelet packet approach to transient signal classification, Appl. Comput. Harmon. Anal., 265, 10.1006/acha.1995.1019

Kutlu, 2012, Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients, Comput. Methods Programs Biomed., 257, 10.1016/j.cmpb.2011.10.002

Kandaswamy, 2004, Neural classification of lung sounds using wavelet coefficients, Comput. Biol. Med., vol. 34, 523, 10.1016/S0010-4825(03)00092-1

Subasi, 2007, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Syst. Appl., 32, 1084, 10.1016/j.eswa.2006.02.005

Subasi, 2012, Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines, Comput. Biol. Med., 42, 806, 10.1016/j.compbiomed.2012.06.004

Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324

Boser, 1992, A training algorithm for optimal margin classifiers, Fifth Annual Workshop on Computational Learning Theory, Pittsburgh : ACM, 10.1145/130385.130401

Vapnik, 1995

Chun-mei, 2012, Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman?Pearson criteria and a support vector machine, Physica A, vol. 391, 1602, 10.1016/j.physa.2011.09.010

Haykin, 1998

Mitchell, 1997

Aha, 1991, Instance-Based learning algorithms, Mach. Learn., 6, 37, 10.1007/BF00153759

Teixeira, 2014, Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients, Comput. Methods Programs Biomed., 324, 10.1016/j.cmpb.2014.02.007

Qiao, 2009, Adaptive weighted learning for unbalanced multicategory classification, Biometrics, 65, 159, 10.1111/j.1541-0420.2008.01017.x

Andrzejak, 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, 64, 10.1103/PhysRevE.64.061907

Tafreshi, 2008, Epileptic seizure detection using empirical mode decomposition

Martis, 2012, Application of empirical mode decomposition (emd) for automated detection of epilepsy using eeg signals, Int. J. Neural Syst., 22, 10.1142/S012906571250027X

Wu, 2009, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adapt. Data Anal., 1, 1, 10.1142/S1793536909000047

Patnaik, 2008, Epileptic EEG detection using neural networks and post-classification, Comput. Methods Programs Biomed., 100, 10.1016/j.cmpb.2008.02.005

Qidwai, 2014, Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data, The 15th International Conference on Biomedical Engineering, 10.1007/978-3-319-02913-9_105