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

• Published in: Medical & biological engineering & computing Vol. 57; no. 6; pp. 1323 - 1339
• Main Authors: Singh, Gurwinder, Singh, Birmohan, Kaur, Manpreet
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification; Classifiers; Computer Applications; Convulsions & seizures; Decomposition; EEG; Electroencephalography; Empirical analysis; Epilepsy; Feature extraction; Human Physiology; Imaging; Learning algorithms; Learning theory; Machine learning; Neural networks; Neurological diseases; Optimization; Optimization algorithms; Original Article; Parameters; Radiology; Recording; Seizing; Seizures; Signal classification; State of the art; Support vector machines; Empirical mode decomposition; Extreme learning machine; Epilepsy; Nearest neighbor; Random forest; Grasshopper optimization algorithm; Support vector machine; Artificial neural network; Intrinsic mode functions; k-Nearest neighbor
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-019-01951-w

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