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

• Published in: Biomedical signal processing and control Vol. 63; p. 102102
• Main Authors: Sharma, Neelam, Kolekar, Maheshkumar H., Jha, Kamlesh
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2021
• Subjects: Dementia; Electroencephalogram; Mild cognitive impairment; Motor speed test; Support vector machine; Motor speed test; Support vector machine; Electroencephalogram; Mild cognitive impairment; Dementia
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.102102

[Display omitted] •An experimental protocol is introduced for clinical setup for dementia diagnosis.•An efficient set of EEG features classify dementia in different cognitive states.•Higher frequencies were prominent in the dementia group during cognitive events.•Motor speed test (MST) outperform over resting state and relaxing state EEG.•Classifier model achieved maximum 92.36% accuracy of dementia diagnosis with MST. 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 age-matched 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 the resting state, 90.23% in relaxing state, and 91.60% in 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.