Key result
Fused cross entropy and neural network model achieves ~98% accuracy classifying Alzheimer's and frontotemporal dementia.
Why the study?
To contribute to the development of computer-aided diagnosis systems to aid in the early diagnosis of Alzheimer disease and frontotemporal dementia by examining EEG signals.
Observational (n=88)
A novel approach using fused cross entropy and artificial neural networks on EEG signals provides highly accurate classification of Alzheimer's and frontotemporal dementia.
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.
No takes yet. Share an insight, caveat, or question.
Lati̇foğlu et al. (2025) conducted an observational in Alzheimer's disease and frontotemporal dementia (n=88). Fused cross entropy (FCE) and artificial neural network (ANN) model was evaluated on Classification of Alzheimer's disease, frontotemporal dementia, and healthy controls using area under the curve (AUC) and accuracy rates. A fused cross entropy and artificial neural network model achieved 98.46% accuracy for classifying Alzheimer's disease vs healthy controls and 98.10% for frontotemporal dementia vs healthy controls.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: