EEG signal analysis for the classification of Alzheimer's and frontotemporal dementia: a novel approach using artificial neural networks and cross-entropy techniques.

Abstract

Dementia, a neurological disorder, can cause cognitive decline due to damage to the brain. Our study aims to contribute to the development of computer-aided diagnosis (CAD) systems to aid in the early diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) by examining Electroencephalogram (EEG) signals. EEG signals of 36 AD, 23 FTD and 29 healthy control (HC) participants were analyzed and entropy measurement approaches were used to analyze the connectivity between EEG channel pairs. The cross-permutation entropy (CPE) method and the cross conditional entropy (CCE) method were analyzed separately and the fused cross entropy (FCE) method was also tested by combining these techniques to determine the most appropriate method for feature extraction from EEG signals. The features obtained from these techniques were then evaluated in the classification phase using two separate machine learning algorithms. According to the performance evaluation criteria, the FCE and artificial neural network (ANN) model showed the most successful performance in the classification of all groups. In terms of area under the curve (AUC) and accuracy rates, 99.85% AUC and 98.46% accuracy were obtained in AD&HC groups, 99.71% AUC and 98.10% accuracy in FTD&HC groups and 99.39% AUC, 96.61% accuracy in AD&FTD groups. With the same model, an AUC rate of 97.14% and accuracy rate of 73.86% was obtained for the classification of the triple group (AD&FTD&HC). It has been observed that the results of this study show successful performance compared to the results of similar studies.