Classifying Alzheimers Disease and Dementia Patients Using Non-invasive EEG Biomarkers

Researchers are currently exploring methods to detect early-stage Alzheimer's Disease (AD) and other forms of dementia such as frontotemporal dementia (FTD), especially through non-invasive biomarkers, i.e., measurements that reflect bio- logical processes. This paper utilizes a dataset of electroencephalogram (EEG) recordings, a noninvasive biomarker, to distinguish individuals with Alzheimer's or frontotemporal dementia from healthy control subjects. This paper explores the usage of machine learning methods to more accurately predict the cognitive status of patients from these non-invasive EEG Biomarkers. We found that AD patients could be easily separated from healthy controls based on their EEG features using simple linear classifiers with an accuracy of 77% and the AD, FTD, and healthy controls with an accuracy of around 57% (randomly selecting the right class is about one third or 33% in the 3-way classification).