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
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm15010027

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 the need for automated approaches. Methods: To address this gap, this study proposes a novel deep learning methodology for EEG classification of AD, FTD, and control (CN) signals. The approach incorporates advanced preprocessing techniques and CNN classification of FFT-based spectrograms and is evaluated using the leave-N-subjects-out validation, ensuring robust cross-subject generalizability. Results: The results indicate that the proposed methodology outperforms state-of-the-art machine learning and EEG-specific neural network models, achieving an accuracy of 79.45% for AD/CN classification and 80.69% for AD+FTD/CN classification. Conclusions: These results highlight the potential of EEG-based deep learning models for early dementia screening, enabling more efficient, scalable, and accessible diagnostic tools.