ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2016
Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- EEG-based biomarker for the identification of mild cognitive impairment via coherency analysis and iterative dynamic time warping.Scientific reports · 2026Article
- Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study.Brain communications · 2026Article
- Atrophy patterns of deep gray matter nuclei in Alzheimer's disease and frontotemporal dementia.Journal of Alzheimer's disease : JAD · 2025Article
- Reliability and state-dependency of EEG connectivity, complexity and network characteristics.Scientific reports · 2025Article
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device.Scientific reports · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Quantitative electroencephalography in the diagnosis of dementia with Lewy bodies.Clinical neurophysiology practice · 2025Review
- Resting-state EEG predicts cognitive decline in a neuropathologically diagnosed longitudinal community autopsied cohort.Parkinsonism & related disorders · 2024Article
- Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls.Schizophrenia (Heidelberg, Germany) · 2023Article
- Electroencephalogram-Based Brain Connectivity Analysis in Prolonged Disorders of Consciousness.Neural plasticity · 2023Article
- Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods.Scientific reports · 2022Article
- Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia.Frontiers in computational neuroscience · 2022Article
- Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2021Review
- Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study.Frontiers in aging neuroscience · 2021Article
- Synaptic Molecular and Neurophysiological Markers Are Independent Predictors of Progression in Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2021Article
- Routine magnetoencephalography in memory clinic patients: A machine learning approach.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Cognitive screening with functional assessment improves diagnostic accuracy and attenuates bias.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- The Role of EEG in the Diagnosis, Prognosis and Clinical Correlations of Dementia with Lewy Bodies-A Systematic Review.Diagnostics (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe aim of this study was to build a random forest classifier to improve the diagnostic accuracy in differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and to quantify the relevance of multimodal diagnostic measures, with a focus on electroencephalography (EEG).
methodsA total of 66 DLB, 66 AD patients, and 66 controls were selected from the Amsterdam Dementia Cohort. Quantitative EEG (qEEG) measures were combined with clinical, neuropsychological, visual EEG, neuroimaging, and cerebrospinal fluid data. Variable importance scores were calculated per diagnostic variable.
resultsFor discrimination between DLB and AD, the diagnostic accuracy of the classifier was 87%. Beta power was identified as the single-most important discriminating variable. qEEG increased the accuracy of the other multimodal diagnostic data with almost 10%. DISCUSSION: Quantitative EEG has a higher discriminating value than the combination of the other multimodal variables in the differentiation between DLB and AD.
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