Latifoğlu Fatma, Orhanbulucu Fırat, Murugappan Murugappan, Gürbüz Sümeyye Nur, Çayır Burçin, Avcı Fatma Zehra
Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, Türkiye.
Neural Informatics Technologies Inc., Kayseri, Türkiye.
Int J Neurosci. 2025 Jul 7:1-17. doi: 10.1080/00207454.2025.2529301.
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 analysed 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 analysed 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.
痴呆症是一种神经疾病，会因大脑受损导致认知能力下降。我们的研究旨在通过检测脑电图（EEG）信号，为计算机辅助诊断（CAD）系统的开发做出贡献，以帮助早期诊断阿尔茨海默病（AD）和额颞叶痴呆（FTD）。分析了36名AD患者、23名FTD患者和29名健康对照（HC）参与者的EEG信号，并使用熵测量方法分析EEG通道对之间的连通性。分别分析了交叉排列熵（CPE）方法和交叉条件熵（CCE）方法，并通过结合这些技术测试了融合交叉熵（FCE）方法，以确定从EEG信号中提取特征的最合适方法。然后，在分类阶段使用两种不同的机器学习算法对从这些技术中获得的特征进行评估。根据性能评估标准，FCE和人工神经网络（ANN）模型在所有组的分类中表现最为成功。在曲线下面积（AUC）和准确率方面，AD&HC组的AUC为99.85%，准确率为98.46%；FTD&HC组的AUC为99.71%，准确率为98.10%；AD&FTD组的AUC为99.39%，准确率为96.61%。使用相同模型对三组（AD&FTD&HC）进行分类时，AUC率为97.14%，准确率为73.86%。据观察，与类似研究的结果相比，本研究的结果显示出成功的表现。
