Journal Article

Selection of optimal wavelet features for epileptic EEG signal classification with LSTM

Aliyu, Ibrahim, Lim, Chang Gyoon

2023

Overview

Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of the model in terms of trainable parameters while still maintaining high accuracy. One way to achieve this is to select the minimum possible number of features. In this paper, we propose a long short-term memory (LSTM) network for the classification of epileptic EEG signals. Discrete wavelet transform (DWT) is employed to remove noise and extract 20 eigenvalue features. The optimal features were then identified using correlation and P value analysis. The proposed method significantly reduces the number of trainable LSTM parameters required to attain high accuracy. Finally, our model outperforms other proposed frameworks, including popular classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT).

• Publisher: Springer London,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial Intelligence / Classification / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Convulsions & seizures / Data collection / Data Mining and Knowledge Discovery / Decision trees / Deep learning / Discrete Wavelet Transform / Eigenvalues / Electroencephalography / Epilepsy / Feature extraction / Feature selection / Image Processing and Computer Vision / Mathematical models / Model accuracy / Neural networks / Neurological diseases / Parameters / Probability and Statistics in Computer Science / S.I. : 2019 India Intl. Congress on Computational Intelligence / Signal classification / Support vector machines / Value analysis / Wavelet transforms