IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network

Epilepsy, a highly prevalent neurological disorder, profoundly impacts the lives of patients with periodic and unexpected seizures that lead to serious injury or death. This research work introduces a Siamese Convolutional Fire Hawk Sparse Autoencoder Network (SCFHSAN) to detect different states of the epileptic seizures. Initially, the EEG signals are gathered from two datasets including TUH EEG and UoB datasets. Then, artifacts removal is performed by multi-resolutional analysis and adaptive filtering technique. After that, dandelion tunable Q-wavelet transform is used to decompose signals into frequency sub-bands. Following that, the feature extraction and feature selection are processed using several techniques. Finally, Siamese convolutional sparse autoencoder network is proposed for epileptic seizure detection and fire hawk optimization algorithm is employed to optimize the weight parameter of the network. The results indicate that the introduced approach achieves an accuracy 99.95% and 98.78% on UoB and TUH EEG datasets, respectively. Analysis determines that the introduced SCFHSAN scheme would help neuro-experts in diagnosing epileptic behavior by analyzing seizure details from various brain regions using multichannel EEG signals.