Adaptive rag-bull rider: A modified self-adaptive optimization algorithm for epileptic seizure detection with deep stacked autoencoder using electroencephalogram. (February 2021)

• Record Type: Journal Article
• Authors: Prabin Jose, J.; Sundaram, M.; Jaffino, G.
• Is Part Of: Biomedical signal processing and control. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: Electroencephalogram -- Deep-stacked autoencoder -- Linear prediction coefficient -- Epileptic seizure -- Taylor-AMS features; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102322
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Proposes an Adaptive rag-Rider optimization algorithm (Adaptive rag-ROA)-based Deep SAE to detect seizures from EEG. Here, Adaptive rag-ROA is used to train deep-stacked autoencoder (Deep SAE) for discovering epileptic seizures. Adaptive rag-ROA is devised by incorporating the Adaptive concept in rag-ROA. The analysis is carried out on TUH EEG Epilepsy Corpus and CHB-MIT Scalp EEG Database. The proposed Adaptive rag-ROA-based Deep SAE has the accuracy of 91.5%, sensitivity of 85.2%, and specificity of 86%. Abstract: Electroencephalogram (EEG) signal is widely adapted for monitoring epilepsy to rejuvenate the close-loop brain. Various conventional techniques are devised for identifying seizures that depend on visually analyzing EEG signals that are an expensive and complicated process if there is a rise in numbers of the channel. A new technique, namely Adaptive rag-Rider optimization algorithm (Adaptive rag-ROA) is presented to train deep-stacked autoencoder (Deep SAE) for discovering epileptic seizures. Initially, the EEG signals are given to the pre-processing module, in which noise has been removed by the bandpass filter. Then, the noise removed signals are provided as an input wherein the EEG is divided into various channels and each channel performs the extraction of features. Here, the features such as Taylor-based delta AMS, Holoentropy, fluctuation index, relative energy, tonal power ratio, spectral features, and linear prediction coefficient (LPC) are acquired from each channel. Furthermore, the Probabilistic principal component analysis (PPCA) is adapted to diminish the dimensionality of features. The obtained feature vectors are fed to Deep SAE for epileptic seizure recognition. The Deep SAE training is carried out with Adaptive rag-ROA that is devised by incorporating the Adaptive concept in rag-ROA. Thus, the output generated from the proposed Adaptive rag-ROA-based Deep SAE is adapted to detect seizures from EEG. The proposed Adaptive rag-ROA-based deep SAE outperformed revealing the highest accuracy of 91.5%, the sensitivity of 85.2%, and specificity of 86%.