Background: Cognitive decline remains highly underdiagnosed in the community despite extensive efforts to find novel biomarkers to detect it. Finding objective screening tools for cognitive decline may improve early diagnosis of Alzheimer′s disease (AD) in the community. EEG biomarkers based on machine learning (ML) may offer a noninvasive low-coast approach for identifying cognitive decline with clinically useful accuracy. However, most of the studies use multi-electrode systems which are not vastly accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with a short interactive cognitive assessment tool. Methods: Seniors in different clinical stages of cognitive decline (healthy to mild dementia, n=60) and young healthy participants (n=22) performed a cognitive assessment which included auditory detection and resting state tasks, while being recorded with a single-channel EEG (Aurora by Neurosteer®). Seniors′ MMSE scores were obtained by clinicians and used in allocating the groups (Healthy: MMSE>28; MCI-R: 28>MMSE>24; and MD: MMSE<24). Data analysis included standard frequency bands as well as three novel biomarkers, A0, ST4 and VC9, previously extracted from a different dataset to minimize overfitting risks. Results: Correlation between MMSE scores and reaction times was significant, validating the cognitive assessment tool. Individual MMSE scores correlated significantly with two of the EEG biomarkers: A0 and ST4. Furthermore, A0 and ST4 showed significant separation between groups of seniors with high vs. low MMSE scores, as well as the healthy young group. ST4 separated between the healthy groups (young and seniors) and the low MMSE (MD) group. Conversely, A0 differentiated between the healthy young group and all three groups of seniors. In the healthy young group, activity of Theta band and VC9 biomarker significantly increased with higher cognitive load, with both separating between the high cognitive load task and resting state. Furthermore, VC9 showed a finer separation between high and low cognitive load levels within the cognitive task. This was not shown in the senior groups, suggesting VC9 may be indicative to cognitive decline in the senior population. Conclusions: These results introduce novel biomarkers which potentially detect cognitive decline, obtained by a wearable single-channel EEG with a short interactive cognitive assessment. Such objective screening tools can be used on a large scale to detect cognitive decline and potentially allow early diagnosis of AD in every clinic.
BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.MethodsThis study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.ResultsMMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24–27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.DiscussionThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.Trial RegistrationNIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].
BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel biomarkers for detection. EEG biomarkers based on machine learning may offer a noninvasive low-coast approach for identifying cognitive decline. However, most studies use multi-electrode systems which are less accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with an interactive assessment tool.MethodsThis pilot study included data collection from 82 participants who performed a cognitive assessment while being recorded with a single-channel EEG system. Seniors in different clinical stages of cognitive decline (healthy to mild dementia) and young healthy participants were included. Seniors’ MMSE scores were used to allocate groups with cutoff scores of 24 and 27. Data analysis included correlation analysis as well as linear mixed model analysis with several EEG variables including frequency bands and three novel cognitive biomarkers previously extracted from a different dataset. ResultsMMSE scores correlated significantly with reaction times, as well as two EEG biomarkers: A0 and ST4. Both biomarkers showed significant separation between study groups: ST4 separated between the healthy senior group and the low-MMSE group. A0 differentiated between the healthy senior group and the other three groups, showing different cognitive patterns between different stages of cognitive decline as well as different patterns between young and senior healthy participants. In the healthy young group, activity of Theta, Delta, A0 and VC9 biomarkers significantly separated between high and low levels of cognitive load, consistent with previous reports. VC9 and Theta showed a finer separation between low cognitive load levels and resting state.ConclusionsThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use portable single-channel EEG device with an interactive cognitive assessment. The short set-up time and novel biomarkers enable objective and easy assessment of cognitive decline. Future studies should explore potential usefulness of this tool in characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.Trial registrationTrial was retrospectively registered in both: NIH Clinical Trials Registry, number NCT04386902, first posted on May 13, 2020 ; Israeli Ministry of Health (MOH) registry number MOH_2019-10-07_007352, first posted on Oct 07, 2019.
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