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

•H-ICA is used to reduce computational complexity for effective feature extraction.•Enhanced autism and epileptic seizure detection through CFCA.•Robust dimensionality reduction scheme for large scale EEG datasets. The 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). The 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. The evaluation of the proposed algorithm achieves an accuracy of 99.51 % and a Sensitivity of 98.86 % and F1 score of 99.32 % The 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. The 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.