Prediction of Cognitive Impairment in Epilepsy Patients Based on EEG Signal with Residual Block-Based Network

Mild cognitive impairment (MCI) is an irreversible, gradual neurological disease and one of the main complications of epilepsy. The scale proves effective in detecting cognitive impairment, but it relies on labor-intensive processes and is easily affected by patients' subjective behavior. Using machine learning methods to analyze EEG data is a promising alternative for detecting MCI. However, the increasing amount of EEG data affects the efficiency of detection. We innovatively propose a novel deep learning (DL) network based on residual blocks to overcome this issue. The DL network aims to efficiently detect cognitive impairment in epilepsy patients by analyzing unique EEG data collected during the Attention Network Test (ANT) and predicting patients' scale score. The suggested network consists of four phases: band-pass filter, spatial convolution block, residual block, and classifier. The entire proposed framework comprises four steps: collecting EEG data, preprocessing the raw data, extracting data features, and classification between MCI subjects and normal ones. Data from 92 patients with epilepsy were used for training and performance evaluation of the network. The classification performance of the proposed network has been compared with that of ResNet18, ResNet34, EEGNet, and FBCNet. The experimental results shows that the proposed network achieved the best classification performance among all five tested networks. The proposed network could also be used for MCI prediction, in which task it achieved about 0.05 in MAE while predicting the score of MOCA for patients, demonstrating a considerable predictive result. Five- fold cross-validation was used to assess the framework's stability.