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Classification of Patients with Alzheimer's Disease and Dementia with Lewy Bodies using Resting EEG Selected Features at Sensor and Source Levels: A Proof-of-Concept Study

Lookup NU author(s): Professor John-Paul TaylorORCiD, Professor Ian McKeith


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Copyright© Bentham Science Publishers; For any queries, please email at BACKGROUND: Early differentiation between Alzheimer's disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. OBJECTIVE: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. METHODS: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). RESULTS: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. CONCLUSION: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.

Publication metadata

Author(s): San-Martin R, Fraga FJ, Del Percio C, Lizio R, Noce G, Nobili F, Arnaldi D, D'Antonio F, De Lena C, Guntekin B, Hanoglu L, Taylor JP, McKeith I, Stocchi F, Ferri R, Onofrj M, Lopez S, Bonanni L, Babiloni C

Publication type: Article

Publication status: Published

Journal: Current Alzheimer Research

Year: 2021

Volume: 18

Issue: 12

Pages: 956-969

Online publication date: 20/12/2021

Acceptance date: 02/04/2018

ISSN (print): 1567-2050

ISSN (electronic): 1875-5828

Publisher: Bentham Science Publishers Ltd


DOI: 10.2174/1567205018666211027143944

PubMed id: 34711165


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