An IoT based Novel Hybrid Seizure Detection Approach for Epileptic Monitoring

This research focuses on a new Electroencephalogram (EEG)-based system for the early detection of epileptic episodes that is made possible by the Internet of Things (IoT). The system is made up of two important units, namely, 1) the multichannel EEG recording unit; and 2) the seizure detection unit. Since epileptic information is more useful when included in multichannel EEG data from many brain regions, the primary goals of this work are: designing and developing the seizure detection unit, making use of Spike-Statistical (SS)- Flower Pollination Algorithm (FPA) based Critical Spectral Verge (CSV) derived features termed as SS-CSV; and employing the convolutional neural network (CNN) method in an IoT-enabled EEG monitoring system to detect EEG seizures. The presented system performed better, with an average accuracy of 98.48% with the CNN classifier. Neuroexperts will find this approach very useful to analyse seizure information, especially in wearable medical devices.