IoT-enabled EEG-based Epilepsy Detection using Multilayer Deep Learning and the Evolutionary Algorithm Approach

Abnormal signals of brain activity can predict epilepsy, which can be effectively detected with the use of IoT-enabled Electro-Encephalo-Gram (EEG) devices. In this process, wearable devices can collect relevant data and transmit them to health providers for analysis. These data can be assessed for epilepsy using Deep Learning (DL) algorithms. DL and evolutionary algorithms are combined to detect epilepsy detection with optimized performance. This study proposed a system with multiple objectives. First, EEG signals were obtained using IoT from subjects in healthy conditions and with epilepsy. In preprocessing, the EEG signal is filtered using finite impulse response. Features were extracted from preprocessed signals, including wavelet coefficients, signal entropy, spectral power, coherence, and frequency bands. An optimal structure was selected from the extracted features through a newly designed hybrid optimization model, called the alpha bat customized squirrel optimizer, with a combination of standard jellyfish search algorithm with particle swarm optimization. Finally, a multimodal deep learning framework, including Long Short-Term Memory Network (LSTM), Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN), detects epilepsy. The results show that the proposed multilayer DL-based approach outperforms existing methods in terms of accuracy, precision, sensitivity, False Negative Rate (FNR), and specificity.