Detection of epilepsy with Electroencephalogram using rule-based classifiers

Epilepsy is a common neurological disorder, characterized by recurrent seizures. Electroencephalogram (EEG), a useful measure for analysing the brain's electrical activity, has been widely used for the detection of epileptic seizures. Most existing classification techniques are primarily aimed at increasing detection accuracy, while the interpretability of the methods have received relatively little attention. In this work, we concentrate on the epileptic classification of EEG signals with interpretability. We propose an epilepsy detection framework, followed by a comparative study under this framework to evaluate the accuracy and interpretability of four rule-based classifiers, namely, the decision tree algorithm C4.5, the random forest algorithm (RF), the support vector machine (SVM)-based decision tree algorithm (SVM+C4.5), and the SVM-based RF algorithm (SVM+RF), in two-group, three-group, and–the most challenging of all–five-group classifications of EEG signals. The experimental results showed that RF outperformed the other three rule-based classifiers, achieving average accuracies of 0.9896, 0.9600, and 0.8260 for the two-group, three-group, and five-group seizure classifications respectively, and exhibiting higher interpretability.

[1]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[2]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[4]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[5]  U. Rajendra Acharya,et al.  Application of Empirical Mode Decomposition (EMD) for Automated Detection of epilepsy using EEG signals , 2012, Int. J. Neural Syst..

[6]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[7]  Daniel Rivero,et al.  Classification of EEG signals using relative wavelet energy and artificial neural networks , 2009, GEC '09.

[8]  V. Srinivasan,et al.  Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features , 2005, Journal of Medical Systems.

[9]  Zhaohong Deng,et al.  Transductive domain adaptive learning for epileptic electroencephalogram recognition , 2014, Artif. Intell. Medicine.

[10]  Hojjat Adeli,et al.  Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection , 2007, IEEE Transactions on Biomedical Engineering.

[11]  W. Hauser,et al.  An estimate of the prevalence of psychogenic non-epileptic seizures , 2000, Seizure.

[12]  Kup-Sze Choi,et al.  Using sequential floating forward selection algorithm to detect epileptic seizure in EEG signals , 2012, 2012 IEEE 11th International Conference on Signal Processing.

[13]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[14]  Andrew P. Bradley,et al.  Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[15]  U. Rajendra Acharya,et al.  Automatic Identification of Epileptic and Background EEG Signals Using Frequency Domain Parameters , 2010, Int. J. Neural Syst..

[16]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[17]  Bart Baesens,et al.  Decompositional Rule Extraction from Support Vector Machines by Active Learning , 2009, IEEE Transactions on Knowledge and Data Engineering.

[18]  Xiaofeng Wang,et al.  A Novel Density-Based Clustering Framework by Using Level Set Method , 2009, IEEE Transactions on Knowledge and Data Engineering.

[19]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[20]  Senlin Luo,et al.  Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes , 2015, IEEE Journal of Biomedical and Health Informatics.

[21]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[22]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[23]  Kemal Polat,et al.  A novel data reduction method: Distance based data reduction and its application to classification of epileptiform EEG signals , 2008, Appl. Math. Comput..

[24]  R. Barry,et al.  EEG differences between eyes-closed and eyes-open resting conditions , 2007, Clinical Neurophysiology.

[25]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[26]  Joachim Diederich,et al.  Eclectic Rule-Extraction from Support Vector Machines , 2005 .

[27]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[28]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[29]  Li Shang,et al.  Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network , 2006, Neurocomputing.

[30]  Andrew P. Bradley,et al.  Rule Extraction from Support Vector Machines: A Sequential Covering Approach , 2007, IEEE Transactions on Knowledge and Data Engineering.

[31]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[32]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[33]  Stephen V. Stehman,et al.  Selecting and interpreting measures of thematic classification accuracy , 1997 .

[34]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[35]  Ricardo Tanscheit,et al.  Fuzzy rule extraction from support vector machines , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[36]  Daniel Rivero,et al.  Automatic feature extraction using genetic programming: An application to epileptic EEG classification , 2011, Expert Syst. Appl..

[37]  P. Sunil Kumar,et al.  Frequency Behaviours of Electroencephalography Signals in Epileptic Patients from a Wavelet Thresholding Perspective , 2015 .