A Novel CNN-Based Framework for Alzheimer’s Disease Detection Using EEG Spectrogram Representations

• Published in: Journal of personalized medicine Vol. 15; no. 1; p. 27
• Main Authors: Stefanou, Konstantinos, Tzimourta, Katerina D., Bellos, Christos, Stergios, Georgios, Markoglou, Konstantinos, Gionanidis, Emmanouil, Tsipouras, Markos G., Giannakeas, Nikolaos, Tzallas, Alexandros T., Miltiadous, Andreas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.01.2025; MDPI
• Subjects: Advertising executives; Alzheimer's disease; Artificial intelligence; Biomarkers; Brain research; Classification; Cognitive ability; Deep learning; Dementia; Dementia disorders; Development and progression; Disease; Diseases; EEG; Electroencephalography; Fourier transforms; Frontotemporal dementia; Greece; Machine learning; Magnetic resonance imaging; Memory; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neurophysiology; Older people; Patients; Signal processing; Tomography; Wavelet transforms; Greece; deep learning; spectogram; CNN; Alzheimer’s disease; FFT; EEG; frontotemporal dementia

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dementia (FTD), is slow and resource-intensive, underscoring t...