Enhanced Alzheimer's disease and Frontotemporal Dementia EEG Detection: Combining lightGBM Gradient Boosting with Complexity Features

Alzheimer's disease and Frontotemporal dementia are the two most reported dementia cases. They both are neurodegenerative disorders without cure while existing treatments only halt their progress. Thus, early detection is of crucial importance. In this work, we utilize electroencephalographic signals of AD and FTD patients and propose a classification pipeline to distinguish them from healthy signals. This pipeline consists of Independent Component Analysis as a preprocessing stage, the extraction of time, frequency and complexity features, feature elimination through importance ranking and finally classification through utilizing Gradient Boosting Decision Trees. The proposed methodology achieved 92.27% F1 score in the Dementia versus Control problem, 83.06% in the AD versus Control and 80.67% in the FTD versus Control.