EEG based dementia diagnosis using multi-class support vector machine with motor speed cognitive test

Dementia is the most burdensome disorder in elders. The Dementia diagnosis is the challenging task at the earliest stages of a neurodegenerative disease when cognitive decline does not interfere with daily life activities. This work focuses on the detection of mild cognitive impairment (MCI) by classifying dementia, MCI and agematched normal subjects. The classification is based on a different set of EEG features. The multi-class support vector machine (SVM) used to classify EEG features during resting-state, relaxing-state, and motor speed test (MST) events. This work investigated the efficient set of EEG features to calculate maximum classification accuracy for each cognitive event. The motor speed of subjects evaluated and correlated the difference between the dominant and non-dominant hand reactivity with ageing in MST event. The proposed work achieved the highest overall accuracy of 87.59% of MST event after 85.09% in relaxing state and 80.10% in resting state, The diagnostic accuracy of MCI group achieved 87.22% in resting-state, 89.72% in relaxing state, and 91.23% in MST Similarly, dementia achieved 88.72% accuracy in the resting state, 90.23% in relaxing state, and 92 36% in MST event. The normal group achieved 94.66% accuracy in then resting state, 90.23% in relaxing state, and 91.60%rin MST event. These findings are comparatively higher than the latest research in this area, and MST findings are novel using multi-class SVM. Thus, MST is the most reliable tool for dementia diagnosis in the clinical setting.

Automated summary

Dementia is a significant burden in the elderly population, and its diagnosis is challenging. This research focuses on detecting mild cognitive impairment through EEG features classification, achieving high accuracy rates, particularly in the motor speed test (MST) event. The findings suggest that MST could be a reliable tool for dementia diagnosis in clinical settings.