The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia

Abstract

Introduction: The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Methods: From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. Results: Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. Conclusion: We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.

Differences of electroencephalography wave with eyes-closed between older women with dementia and without dementia

“…An EEG records electrical activity in the brain and measures voltage fluctuations due to the activation of brain neurons. We can use EEG techniques to detect abnormalities (Engedal et al, 2020).…”

A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer’s dementia

“…The background and technical features of the MC-004 are briefly described below. The technical aspects of the algorithm building have been published more in detail elsewhere, albeit addressing another research question [ 21 ]. MC-004 algorithm was established by improving the algorithm described previously [ 13 ] and preliminarily tested in the study by Engedal et al [ 12 ].…”

Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?

“…Therefore, it is not surprising for an AI model to solve the task of AD vs. NC subject classification with high accuracy, when taking into account NPS test results or neuroimaging data [55]. To date, several predictive models have been developed [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72], yielding peak accuracy values of 100% in AD vs. NC classification [55]. In contrast, a much more challenging task for AI is to identify individuals with subjective or mild impairment who will develop AD dementia, with respect to stable MCI or MCI not due to AD, given the shaded differences and the overlapping symptoms in the clinical or biological variables defining these groups in the early phases [73].…”