EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates investigations with respect to every horizon of the due cause for it; further on, the diagnosis and treatments can be advanced with the aid of technology. This work attempts to conduct one such investigation on dementia diagnosis based on EEG signals. The implementation is carried out under three different verticals. Firstly, a conventional machine learning model was developed post-pre-processing, and feature extraction from the power spectral density was done using a Random Forest classifier. Second, 1D Convolutional Neural Networks models are developed, and pre-processed EEG signals are fed as input. Third, stacked spectrogram images are computed from decomposed EEG signals and are fed to 2D CNN models for classification. The investigations are performed on three different benchmark datasets. Across three datasets, the class labels include cognitively normal, frontotemporal dementia, mild cognitive impairment, and Alzheimer's. The study offers a comparative evaluation across three distinct datasets, illustrating that deep learning models, particularly 1D and 2D CNNs, consistently outperform conventional methods in recognizing subtle EEG signal patterns linked to neurodegenerative conditions. For instance, in Dataset 1, the 2D CNN achieved the highest accuracy of 91.13%, surpassing the Random Forest model's 84.78% accuracy. Nevertheless, the investigation also points out challenges in Dataset 3, indicating the necessity for further model optimization tailored to specific datasets. Statistical tests validate the findings. This study showcases a comparative investigation of the potential of deep learning models vs. conventional classifiers in clinical environments for the early diagnosis of dementia.