SPA-IoT with MCSV-CNN: a novel IoT-enabled method for robust pre-ictal seizure prediction.

Abstract

This paper introduces a new approach to real-time epileptic seizure prediction using a lightweight Convolutional Neural Network (CNN) architecture and multiresolution feature extraction from electroencephalogram (EEG) recordings. Multiresolution Critical Spectral Verge CNN (MCSV-CNN), the suggested model, is best suited for use in wearable technology that is connected to the Internet of Things (IoT). The software module uses pre-ictal and inter-ictal EEG segments to forecast seizures early, and the signal acquisition module collects EEG data. Multiscale frequency analysis and spatial feature learning are combined in the MCSV-CNN architecture to capture minute signal changes that precede seizures. Both actual clinical EEG recordings and the Temple University Hospital EEG Seizure Corpus (TUH-EEG) were evaluated. Predicting has been performed using a 5-minute pre-ictal window and a 10-minute seizure occurrence prediction (SOP) horizon. The approach proposed outperformed a number of existing CNN-based seizure prediction techniques with an average prediction accuracy of 99.5%, sensitivity of 98.3%, false prediction rate of 0.045, and a high Area Under the Curve (AUC). These findings show that MCSV-CNN has the potential to be a dependable, real-time seizure prediction tool that could be used practically in wearable medical technology. The prediction accuracy and lightweight architecture of the technology point to its potential application in early clinical intervention and ongoing at-home monitoring.