A significant framework for epilepsy and seizure clusters detection using MSRS-LSTM and FID-DO

Abstract

Epilepsy is a neurological condition that causes unprovoked and recurring seizures. Electroencephalography (EEG) is essential for diagnosing epilepsy, as EEG records the brain's electrical activity. However, the prevailing works are not focused on detecting the multiple seizure occurrences in one person and the Seizure Clusters (SC). So, a significant epilepsy detection framework using Meta-step Rootsig Long Short-Term Memory (MsRs-LSTM) and Fractional integro-differential Duffing Oscillator (Fid-DO) is proposed in this framework. The EEG signals are initially preprocessed and decomposed using Extrema mirror Empirical mode Decomposition (EED). Then, the signals’ peaks are detected, sub-bands are identified, and features are extracted from them. Similarly, from the preprocessed signal, Fid-DO detects weak periodicity, and the features are extracted from the signals being detected. Afterwards, the extracted features’ dimensionality is reduced and given to MsRs-LSTM for epilepsy classification. The evaluation outcomes stated the robustness of the proposed framework in epilepsy detection with 97.56% classification accuracy.