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Grasshopper optimization algorithm-based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals.

Epilepsy is one of the most common neurological disease worldwide. It is diagnosed by analyzing a long electroencephalogram (EEG) recording in a clinical environment, which may be much prone to errors and a time-consuming task. In this paper, a methodology for the classification of an epileptic seiz...

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Published in:Medical & Biological Engineering & Computing Vol. 57; no. 6; pp. 1323 - 1340
Main Authors: Singh, Gurwinder, Singh, Birmohan, Kaur, Manpreet
Format: Journal Article
Published: Springer Nature Jun2019
Online Access:View this record in EBSCOhost
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      dt: Jun2019
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      pub: Springer Nature
      place: New York, New York
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        atl: Grasshopper optimization algorithm-based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals.
      aug:
        au:
          Singh, Gurwinder
          Singh, Birmohan
          Kaur, Manpreet
        affil: Department of Computer Science, Bhai Sangat Singh Khalsa College, Banga, Punjab, India
      sug:
        subj:
          Epilepsy
          Electroencephalography
          Algorithms
          Epilepsy Diagnosis
          Chaos Theory
          Physics
          Scales
      ab: Epilepsy is one of the most common neurological disease worldwide. It is diagnosed by analyzing a long electroencephalogram (EEG) recording in a clinical environment, which may be much prone to errors and a time-consuming task. In this paper, a methodology for the classification of an epileptic seizure is proposed for analyzing EEG signals. EEG signal is decomposed into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). A fusion, of the extracted non-linear and spike-based features from each of the IMF signals, is made. The parameters of five machine learning algorithms; k-nearest neighbor (k-NN), extreme learning machine (ELM), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) are optimized, as well as a set of the significant features is chosen using grasshopper optimization algorithm (GOA). These classifiers with their optimized parameters are ensembled together for the classification of epileptic seizures. The results show that ensemble classifier performs better than individual classifier. A comparison of the proposed methodology with state of the art epileptic seizure detection techniques is also made for validation. Graphical abstract ᅟ.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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