Application of machine learning in EEG-based dementia diagnosis: Classification and differential diagnosis

BackgroundElectroencephalogram (EEG) is a promising, non-invasive method for identifying the presence of Alzheimer's disease (AD) by recognizing specific brain activity patterns associated with the disease. However, research on the correlation between EEG and the degree of cognitive impairment is still lacking.ObjectiveIn this study, we employ machine learning models to explore the potential of EEG in distinguishing different levels of cognitive impairment and various types of dementia.MethodsA total of 431 participants, including 77 cognitively unimpaired (CU), 167 patients with mild dementia, 110 patients with moderate dementia, and 77 patients with severe dementia were enrolled. Among them, 91 patients have detailed biomarker results to support differential diagnosis, with 77 AD and 14 frontotemporal dementia. After feature extraction, the rule-based representation learning was used to train models for EEG-based classification tasks.ResultsOur model can effectively differentiate between CU and moderate-to-severe dementia (AUC 0.8475), as well as between CU and AD patients in individuals under 65 (AUC 0.8170). However, our preliminary analysis was not able to effectively distinguish between different types of dementia. It is also challenging to differentiate between CU and mild dementia groups, as well as between the moderate and severe dementia.ConclusionsOur study suggests that EEG might be used not only in the early identification of AD, but also in the diagnosis and monitoring of the entire dementia spectrum, encompassing various stages and types of cognitive decline.