Chapter 7 - Dementia diagnosis with EEG using machine learning

Abstract

Dementia is a group of symptoms caused by neurodegenerative disease. It is characterized by impairment in memory, reasoning, behavior, and the capability to perform everyday activities. Worldwide, 50 million people have dementia, and nearly 10 million new dementia cases occur each year. Dementia is a significant reason for disability and dependency in late life. Dementia has a physical, psychological, social, and economic influence on dementia patients and their careers, families, and society. Therefore, there is a need for automated early dementia diagnosis that has cognitive as well as electroencephalogram (EEG) components. State-of-the-art methods have been proposed for efficient dementia diagnosis using machine learning (ML) and deep learning (DL) algorithms with imaging data. Usually, imaging diagnosis misses the early signs of neurodegenerative disease; however, these signs are clearly visible in a psychophysiological experiment. Datasets for dementia diagnosis using cognitive tasks are limited, but some recent research has shown significant results using different cognitive tests. Many other EEG-based ML techniques have achieved good accuracy in early dementia diagnosis, but there is still no final solution. This chapter summarizes all the work done to date for dementia diagnosis based on EEG and cognitive task data and compares various ML approaches used in this regard. It also summarizes different ML approaches with advanced EEG signal processing that can guide future researchers, practitioner, and technicians.