A Heuristic-Concatenated Feature Classification Algorithm (H-CFCA) for autism and epileptic seizure detection

Abstract

ObjectiveThe objective is to implement a comprehensive Heuristic-CFCA (H-CFCA) a feature classification system using Machine Learning and Deep Learning principles for detecting Autism and Epileptic Seizures with EEG data. This involves a sequential integration of functional blocks that includes EEG Pre-Processing, Dimensionality Reduction, Feature Extraction, and a Concatenated Feature Classification Scheme (CFCA). MethodsThe methodology involves db4 Wavelet-based decomposition (WD) for pre-processing the EEG into five frequency bands, followed by Singular Value Decomposition (SVD) for dimensionality reduction. Subsequently, Heuristic Independent Component Analysis (H-ICA) is applied to extract Independent Components (ICs). These ICs are fed to H-CFCA comprises of Support Vector Machine (SVM) as first stage classifier, followed by a Bi-Stack Long Short-Term Memory (BiS-LSTM) as second stage classifier to detect the presence of autism and epileptic seizure. ResultsThe evaluation of the proposed algorithm achieves an accuracy of 99.51 % and a Sensitivity of 98.86 % and F1 score of 99.32 % ConclusionThe article realizes an effective and feasible H-CFCA, with an ability to handle large size EEG datasets and achieves reduced computational complexity, and improved feature classification. Comparative analysis shows that H-CFCA achieves the highest accuracy and sensitivity compared to existing ML/DL-based algorithms. SignificanceThe EEG feature classification system significantly advances diagnostic accuracy, improves social well-being by enabling personalized interventions, and drives technological innovation in healthcare by leveraging advanced classification methods and data analysis techniques.