Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features

Kashefpoor, Masoud; Rabbani, Hossein; Barekatain, Majid

doi:10.4103/2228-7477.175869

Cited by 68 publications

(21 citation statements)

References 30 publications

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“…Results demonstrated the ability of A0 and ST4 to signi cantly differentiate between groups of seniors with high vs. low MMSE scores. Speci cally, ST4 separated between the healthy seniors and low-MMSE seniors (MD group) with the common cutoff score of 24, comparable to previous reports in this eld [16], [20], [21]. Conversely, A0 showed differences between the healthy seniors and all other groups.…”

Section: Discussionsupporting

confidence: 84%

“…The biomarkers used here were calculated on a different dataset to avoid the risks associated with classi cation studies such as over tting [32]. This is unlike other studies that use classi ers trained and tested via cross validation on the same dataset [10], [20], [21]. Speci cally, the pre-extracted biomarkers used here, ST4 and VC9, were previously considered in a study performed on young healthy subjects.…”

Section: Discussionmentioning

confidence: 99%

“…10 patients signed the ICF and were included in the overall patient count but were excluded from data analysis due to their desire to stop the study or technical problems in the recording. The participants were divided into 3 groups according to the MMSE scores: 17 patients with a score of [17][18][19][20][21][22][23]…”

Section: Senior Participantsmentioning

confidence: 99%

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Detection of Cognitive Decline using a Single-Channel EEG with an Interactive Assessment Tool

Molcho¹,

Maimon

Pressburger³

et al. 2021

Preprint

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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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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Cognitive Decline using a Single-Channel EEG with an Interactive Assessment Tool

Molcho¹,

Maimon

Pressburger³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Results demonstrated the ability of A0 and ST4 to significantly differentiate between groups of seniors with high vs. low MMSE scores. Specifically, ST4 separated between the healthy seniors and low-MMSE seniors (MD group) with the common cutoff score of 24, comparable to previous reports in this field [16], [20], [21]. Conversely, A0 showed differences between the healthy seniors and all other groups.…”

Section: Discussionsupporting

confidence: 84%

“…The biomarkers used here were calculated on a different dataset to avoid the risks associated with classification studies such as overfitting [32]. This is unlike other studies that use classifiers trained and tested via cross validation on the same dataset [10], [20], [21]. Specifically, the pre-extracted biomarkers used here, ST4 and VC9, were previously considered in a study performed on young healthy subjects.…”

Section: Discussionmentioning

confidence: 99%

Detection of cognitive decline using a single-channel EEG with an interactive assessment tool

Molcho

Maimon

Pressburger

et al. 2020

Preprint

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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.

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Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

Deng

Sun

Kavcic

et al. 2023

Alzheimer's & Dementia

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BACKGROUNDEarly discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency.METHODSOur research is based on resting‐state electroencephalography (EEG) and the current dataset includes 137 consensus‐diagnosed, community‐dwelling Black Americans (ages 60–90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time‐varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity‐based score.RESULTSThe leave‐one‐out cross‐validation accuracy is 91.97% and 3‐fold accuracy is 91.17%. The 9 to 18 months’ progression trend prediction accuracy over an availability‐limited subset sample is 84.61%.CONCLUSIONThe EEG‐based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

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Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features

Cited by 68 publications

References 30 publications

Detection of Cognitive Decline using a Single-Channel EEG with an Interactive Assessment Tool

Detection of Cognitive Decline using a Single-Channel EEG with an Interactive Assessment Tool

Detection of cognitive decline using a single-channel EEG with an interactive assessment tool

Novel methodology for detection and prediction of mild cognitive impairment using resting‐state EEG

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