EEG-Based Spectral Dynamic in Characterization of Poststroke Patients with Cognitive Impairment for Early Detection of Vascular Dementia

Abstract

One common type of vascular dementia (VaD) is poststroke dementia (PSD). Vascular dementia can occur in one-third of stroke patients. The worsening of cognitive function can occur quickly if not detected and treated early. One of the potential medical modalities for observing this disorder by considering costs and safety factors is electroencephalogram (EEG). It is thought that there are differences in the spectral dynamics of the EEG signal between the normal group and stroke patients with cognitive impairment so that it can be used in detection. Therefore, this study proposes an EEG signal characterization method using EEG spectral power complexity measurements to obtain features of poststroke patients with cognitive impairment and normal subjects. Working memory EEGs were collected and analyzed from forty-two participants, consisting of sixteen normal subjects, fifteen poststroke patients with mild cognitive impairment, and eleven poststroke patients with dementia. From the analysis results, it was found that there were differences in the dynamics of the power spectral in each group, where the spectral power of the cognitively impaired group was more regular than the normal group. Notably, (1) significant differences in spectral entropy (SpecEn) with a p value <0.05 were found for all electrodes, (2) there was a relationship between SpecEn values and the severity of dementia (SpecEnDem < SpecEnMCI < SpecEnNormal), and (3) a post hoc multiple comparison test showed significant differences between groups at the F7 electrode. This study shows that spectral complexity analysis can discriminate between normal and poststroke patients with cognitive impairment. For further studies, it is necessary to simulate performance validation so that the proposed approach can be used in the early detection of poststroke dementia and monitoring the development of dementia.

Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice

“… 57 , 66 Accordingly, assessing the degree of complexity can provide insights into the brain’s different conditions, reflecting its dynamic nature. 43 , 57 , 67 …”

“… 68 , 69 It was found that Sample Entropy in ischaemic thalamus of stroke subjects was higher at all electrodes with respect to control subjects while resting with eyes closed. 70 , 71 The study by Hadiyoso and collaborators 43 proposed a method for characterizing EEG signals in poststroke patients with cognitive impairment and normal subjects, by measuring EEG spectral power complexity. The key finding included a relationship between Spectral Entropy values and the severity of dementia, demonstrating the ability to differentiate between normal subjects and poststroke patients with cognitive impairment, suggesting its potential as a discriminative tool in this context.…”

“…The sample size was determined with the GPower 3.1.9.4 software considering the mean ± SD of two groups, based on our previous study on entropy measures in human physiological ageing 43 and considering a power of 80%, a confidence interval of 95% and a type I error of 0.05 with two tails.…”

“… 41 , 42 Additionally, previous investigations involving human subjects have consistently indicated heightened brain complexity in stroke patients in comparison to control subjects, during resting-state EEG recordings. 43 , 44 …”

Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes

“…Specific frequency bands, power ratios between different frequency bands, and symmetric index were strongly correlated with patients’ stroke severity index using the National Institutes of Health Stroke Scale (NIHSS) and with the functional status assessed by the modified Rankin Scale ( Sheorajpanday et al, 2011 ; Finnigan and van Putten, 2013 ). Further, several QEEG indices, such as relative theta frequency ( Schleiger et al, 2017 ) or irregularity of spectral power of frontal lobes ( Hadiyoso et al, 2022 ), predicted post-stroke cognitive impairment in the previous studies with limited explanatory power. As incorporating brain network attributes may provide more details about cognitive prognosis after stroke, QEEG using a machine learning approach to incorporate functional connectivity along with lesion characteristics would be a powerful tool that could reveal the network vulnerability for post-stroke cognitive impairment in stroke patients.…”