A novel method to analyze EEG synchrony for the early diagnosis of Alzheimer's disease in optimized frequency bands

Studies have reported that electroencephalogram (EEG) signals in Alzheimer's disease (AD) patients usually have less synchronization as compared to healthy subjects. To detect this perturbation, three neural synchrony measurement techniques; phase synchrony, magnitudes squared coherence, and cross correlation are applied on a dataset for mild Alzheimer's disease (MiAD) patients and healthy subjects. This paper discusses the use of principle component analysis (PCA) before applying neural synchrony measurement techniques and assesses the approach with others using the Mann-Whitney U test. The results show that applying PCA before synchrony measurement techniques improvements are made compared to the use of traditional techniques.