Early Detection Of Stress And Anxiety Based Seizures In Position Data Augmented EEG Signal Using Hybrid Deep Learning Algorithms

Abstract

Epilepsy is a neurological problem due to aberrant brain activity. Epilepsy diagnose through Electroencephalography (EEG) signal. Human interpretation and analysis of EEG signal for earlier detection of epilepsy is subjected to error. Detection of Epileptic seizures due to stress and anxiety is the major problem. Epileptic seizure signal size, and shape changes from person to person based on their stress and anxiety level. Stress and anxiety based epileptic seizure signals vary in amplitude, width, combination of width and amplitude. In this paper, seizures of different size and shape are synthesized using data augmentation for different stress and anxiety level. Different augmentation such as (i) position data augmentation (PDA) (ii) random data augmentation (RDA) applied to BONN EEG dataset for synthetizations of stress and anxiety based epileptic seizure signals. Augment EEG epileptic seizure signals are analyzed using proposed methods such as i) FCM-PSO-LSTM and ii) PSO-LSTM for earlier detection of stress and anxiety-based seizures. The proposed algorithms perform better in earlier detection of stress and anxiety-based seizure signals. The predicted accuracy of proposed methods such as (i) FCM-PSO-LSTM and (ii) PSO-LSTM is about (i)98.5% and (ii) 97% for PDA and RDA is about (i) 98% and (ii) 98.5% for BONN. The accuracy of proposed methods such as(i) FCM-PSO-LSTM and (ii) PSO-LSTM is about (i) 98% and (ii) 97.5% for PDA and RDA is about (i) 97.5% and (ii) 98% respectively for CHB-MIT