Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

• Autorzy: George S. Thomas , Subathra M. S. P. , Sairamya N. J. , Susmitha L. , Joel Premkumar M.

Identyfikatory

DOI

10.1016/j.bbe.2020.02.001

• Języki publikacji: EN

Abstrakty

EN

Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive neural activity which can be diagnosed by inspecting the electroencephalography (EEG) signals visually. The manual inspection of EEG signals is subjected to human error and is a tedious process. Further, an accurate diagnosis of generalized and focal epileptic seizures from normal EEG signals is vital for the supervision of pertinent treatment, life advancement of the subjects, and reduction in cost for the subjects. Hence the development of automatic detection of generalized and focal epileptic seizures from normal EEG signals is important. An approach based on tunable-Q wavelet transform (TQWT), entropies, Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is proposed in this work for detection of epileptic seizures and its types. Two EEG databases namely, Karunya Institute of Technology and Sciences (KITS) EEG database and Temple University Hospital (TUH) database consisting of normal, generalized and focal EEG signals is used in this work to analyze the performance of the proposed approach. Initially, the EEG signals are decomposed into sub-bands using TQWT and the non-linear features like log energy entropy, Shannon entropy and Stein's unbiased risk estimate (SURE) entropy is computed from each sub-band. The informative features from the computed feature vectors are selected using PSO and fed into ANN for the classification of EEG signals. The proposed algorithm for KITS database achieved a maximum accuracy of 100% for four experimental cases namely, (i) normal-focal, (ii) normal-generalised, (iii) normal-focal + generalised and (iv) normal-focal-generalised. The TUH database achieved an accuracy of 95.1%, 97.4%, 96.2% and 88.8% for the four experimental cases. The proposed approach is promising and able to discriminate the epileptic seizure types with satisfactory classification performance.

Słowa kluczowe

EN

tunable-Q wavelet transform; electroencephalography; pattern recognition; particle swarm optimisation; artificial neural network

PL

transformata falkowa; elektroencefalografia; rozpoznawanie wzorca; sztuczna sieć neuronowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 709--728
• Opis fizyczny: Bibliogr. 95 poz., rys., tab., wykr.

Twórcy

autor

George S. Thomas

• Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore - 641 114, India

autor

Subathra M. S. P.

• Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

autor

Sairamya N. J.

• Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

autor

Susmitha L.

• Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

autor

Joel Premkumar M.

• Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

Bibliografia

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Uwagi

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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).