Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Abstract

Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.

Detection of Epileptic Seizures from EEG Signals Using Machine Learning Classifiers

“…For 3-class classification of EEG signals, CNN classifiers can provide accuracies of 94.46% [22] and 97.07% [23], which are much better than 91.70% accuracy obtained with multiscale entropy based SVM [6]. For 5-class classification, the accuracy for using CNN have been reported to be 95.84% in the research work [23], which is also comparable to the classification accuracy of 96% obtained with octal pattern and wavelet transform based KNN [25].…”

Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm

“…The results are arranged in ascending order of publication year. It is observed from [19], Akyol [20], Ayesha and coauthors [23], Sujatha [22], and Tuncer and coauthors [24], it is observed that the proposed study achieves better performance than the existing ones. Our outcomes reflect that the proposed approach has potentials to act as a significant tool to assist clinicians for detecting epilepsy.…”

“…For inter-ictal and ictal EEG signals classification, the highest accuracy of 99.38% was reported using the fuzzy rough nearest neighbor (FRNN). A new feature generation method based on 1D octal pattern for the classification of epileptic seizure is proposed by Tuncer and coauthors in [24]. For selecting EEG features, neighborhood component analysis was used and KNN did the classification.…”

Classification of Epileptic Brain Signals of College Students Based on Deep Learning

“…They use the extracted eigenvalues to classify EEG signals and detect epilepsy. Tuncer T et al [6] proposed a lightweight and simple classification model to automatically detect seizures, and achieved effective results. The accuracy of classification reached 96.0%.…”