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

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, Bhai Sangat Singh Khalsa College, Banga, Punjab, India

    Gurwinder Singh

  2. Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India

    Birmohan Singh

  3. Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India

    Manpreet Kaur

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  1. Gurwinder Singh
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  2. Birmohan Singh
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  3. Manpreet Kaur
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Correspondence to Manpreet Kaur.

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Highlights

• A novel epileptic EEG classification is presented.

• The fusion of non-linear and spike-based features is proposed.

• Grasshopper optimization algorithm is used to select significant features as well as to optimize two parameters of the classifiers.

• The ensemble of five classifiers with their optimized parameters is built to separate normal, interictal, and ictal EEG signals.

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Singh, G., Singh, B. & Kaur, M. Grasshopper optimization algorithm–based approach for the optimization of ensemble classifier and feature selection to classify epileptic EEG signals. Med Biol Eng Comput 57, 1323–1339 (2019). https://doi.org/10.1007/s11517-019-01951-w

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  • DOI: https://doi.org/10.1007/s11517-019-01951-w

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