Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM

Abstract

In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging for radiologists, and there is a limited availability of anxiety-based EEG signals. Data augmentation methods are required to increase the number of novel samples. An epileptic seizure arises due to anxiety, which manifests as variations in EEG signal patterns consisting of changes in the size and shape of the signal. In this study, anxiety EEG signals were synthesized by applying data augmentation methods such as random data augmentation (RDA) to existing epileptic seizure signals from the Bonn EEG dataset. The data-augmented anxiety seizure signals were processed using three algorithms—(i) fuzzy C-means–particle swarm optimization–long short-term memory (FCM-PS-LSTM), (ii) particle swarm optimization–long short-term memory (PS-LSTM), and (iii) parrot optimization LSTM (PO-LSTM)—for the detection of anxiety ESs via EEG signals. The predicted accuracies of detecting ESs through EEG signals using the proposed algorithms—namely, (i) FCM-PS-LSTM, (ii) PS-LSTM, and (iii) PO-LSTM—were about 98%, 98.5%, and 96%, respectively.

Keywords

• EEG epileptic seizure signals
• data augmentation
• fuzzy feature extraction
• parrot optimization
• LSTM classifier