Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer's disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer's disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.
Humans ; Electroencephalography/methods* ; Alzheimer Disease/physiopathology* ; Dementia/physiopathology* ; Early Diagnosis ; Algorithms ; Brain/physiopathology* ; Machine Learning ; Frontotemporal Dementia/physiopathology* ; Diagnosis, Computer-Assisted

Amyotrophic Lateral Sclerosis ; pathology ; physiopathology ; Female ; Frontotemporal Dementia ; pathology ; physiopathology ; Humans ; Male ; Middle Aged ; Psychotic Disorders ; pathology ; physiopathology

Adult ; Female ; Frontal Lobe ; physiopathology ; Frontotemporal Dementia ; diagnosis ; etiology ; physiopathology ; Humans ; Magnetic Resonance Imaging ; Treatment Outcome ; Tuberculoma, Intracranial ; complications