DOI: 10.14704/nq.2018.16.4.1189

A Classification Method for Epileptic Electroencephalogram Based on Wavelet Multi-scale Analysis and Particle Swarm Optimization Algorithm

Yiming Tian, Wei Chen, Xitai Wang


The automatic classification of epileptic electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. In this paper, an epileptic EEG classification method based on wavelet multi-scale analysis and particle swarm optimization is proposed. Firstly, the multi-scale is carried out to the original EEG to extract its sub-bands of different frequency. Secondly, the Hurst exponent and the sample entropy are used to extract the EEG signals and its sub-bands. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the extreme learning machine (ELM), and the obtained eigenvector is put to PSO-ELM to realize the purpose of classification of epileptic EEG. The proposed method in this paper achieved 99.7% classification accuracy for the discrimination between epileptic ictal and interictal EEG, which is superior to those methods in other studies.


Epileptic electroencephalogram; Particle swarm optimization; Wavelet multi-scale analysis; Extreme learning machine; Classification

Full Text:



Aarabi A, Fazel-Rezai R, Aghakhani Y. A fuzzy rule-based system for epileptic seizure detection in intracranial EEG. Clinical Neurophysiology 2009; 120(9):1648-57.

Chisci L, Mavino A, Perferi G, Sciandrone M, Anile C, Colicchio G, Fuggetta F. Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Transactions on Biomedical Engineering 2010; 57(5):1124-32.

Chua EC, Patel K, Fitzsimons M, Bleakley CJ. Improved patient specific seizure detection during pre-surgical evaluation. Clinical Neurophysiology 2011;122(4):672-79.

Fisher RS, Boas WV, Blume W, Elger C, Genton P, Lee P, Engel J. Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 2005; 46(4):470-72.

Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. Journal of Neuroscience Methods 2010; 193(1):156-63.

Iasemidis LD. Epileptic seizure prediction and control. IEEE Transactions on Biomedical Engineering. 2003; 50(5):549-58.

Jouny CC, Bergey GK. Characterization of early partial seizure onset: Frequency, complexity and entropy. Clinical Neurophysiology 2012; 123(4):658-69.

Kennedy J. Particle swarm optimization. InEncyclopedia of machine learning 2011: 760-766. Springer US. Kovacs P, Samiee K, Gabbouj M. On application of rational discrete short time Fourier transform in epileptic seizure classification. IEEE International Conference on Acoustics, Speed and Signal Processing (ICASSP-2014). Florence: IEEE 2014; 4: 5839-43.

Kumar SP, Sriraam N, Benakop PG, Jinaga BC. Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Systems with Applications 2010;37(4):3284-91.

Kumar Y, Dewal ML, Anand RS. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing. 2014;133:271-79.

Soltesz I, Staley K. Computational neuroscience in epilepsy. San Diego, USA, 2008.

Solihin MI, Akmeliawati R, Tijani IB, Legowo A. Robust state feedback control design via PSO-based constrained optimization. Control and Intelligent Systems 2011; 39(3):168.

Sood M, Bhooshan SV. Automatic processing of EEG signals for seizure detection using soft computing techniques. InRecent Advances and Innovations in Engineering (ICRAIE) 2014; 9: 1-6.

Vidyasagar KC, Moghavvemi M, Prabhat TS. Performance evaluation of contemporary classifiers for automatic detection of epileptic EEG. InIndustrial Instrumentation and Control (ICIC), 2015 International Conference on 2015: 372-77.

Xıng X, Lıu H, Huang W. Gait Pattern classification with wavelet energy and sample entropy based on acceleration signals. Chinese Journal of Sensors and Actuators 2013; 4:020.

Yuan Q, Zhou W, Li S, Cai D. Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Research 2011; 96(1-2):29-38.

Yuan Q, Zhou W, Liu Y, Wang J. Epileptic seizure detection with linear and nonlinear features. Epilepsy & Behavior 2012; 24(4):415-21.

Supporting Agencies

This work was supported by National Key Technology Research and Development Program (2015BAI06B00).

| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2017