An Efficient EEG-based System for Multi-Class Neurological Disorder Diagnosis using EMPA Optimized Bi-LSTM

Abstract

Neurological illness detection using electroencephalograms (EEGs) is not an easy task due to the nature of brain signals, which are not stationary and noisy. In order to obtain the correct multi-class classification, the paper develops a hybrid deep learning framework that includes pre-processing, graph-based feature generation and efficient time modelling. The initial step of cleaning the data is to eliminate any missing data, or artefacts in EEG signals to obtain higher resulting output. A Graph Convoluted Network (GCN) is then used to approximate the functional connectivity between the EEG electrodes and each of the channels is represented as a node in a modelled graph. The GCN models spatial relationships and gets trained on features that are of relevance to the neurological patterns. The Bidirectional Long Short-Term Memory (BiLSTM) network receives features to acquire forward and backward statistical relationships. To enhance the convergence and speed of the weight selection process, the Enhanced Marine Predators Algorithm (EMPA) is applied to avoid falling into the local minima. Proposed model is more effective with higher accuracy of precision ,recall value and F1-score value. The experimental results demonstrating a sufficient recall of spatio-temporal behaviour and classification performance make the proposed model useful to be used in clinical decision support systems.