ArticleScientific reports2018
EEG may serve as a biomarker in Huntington's disease using machine learning automatic classification.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Executive Impairment in Huntington's Disease: Insights From a Systematic Review of the Literature.Brain and behavior · 2026Pooled it
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- The use of electroencephalography in neurodegenerative disease and its utility in dementia.NPJ dementia · 2026Review
- Neural Correlates of Huntington's Disease Based on Electroencephalography (EEG): A Mechanistic Review and Discussion of Excitation and Inhibition (E/I) Imbalance.Journal of clinical medicine · 2025Review
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Advances in Huntington's Disease Biomarkers: A 10-Year Bibliometric Analysis and a Comprehensive Review.Biology · 2025Review
- Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review.Journal of clinical medicine · 2024Review
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- The phase coherence of the neurovascular unit is reduced in Huntington's disease.Brain communications · 2024Article
- Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- Uncovering the Genetic and Molecular Features of Huntington's Disease in Northern Colombia.International journal of molecular sciences · 2023Article
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- Sleep and Circadian Rhythm Dysfunction in Animal Models of Huntington's Disease.Journal of Huntington's disease · 2023Review
- Neurophysiological hallmarks of Huntington's disease progression: an EEG and fMRI connectivity study.Frontiers in aging neuroscience · 2023Article
- Wearable chemical sensors for biomarker discovery in the omics era.Nature reviews. Chemistry · 2022Review
- Quantitative electroencephalographic analysis of delirium tremens development following alcohol-withdrawal seizure based on a small number of male cases.Brain and behavior · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Clinical, Imaging, and Laboratory Markers of Premanifest Spinocerebellar Ataxia 1, 2, 3, and 6: A Systematic Review.Journal of clinical neurology (Seoul, Korea) · 2021Review
- Identification of contributing genes of Huntington's disease by machine learning.BMC medical genomics · 2020Article
- Diverse Applications of Artificial Intelligence in Neuroradiology.Neuroimaging clinics of North America · 2020Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reliable markers measuring disease progression in Huntington's disease (HD), before and after disease manifestation, may guide a therapy aimed at slowing or halting disease progression. Quantitative electroencephalography (qEEG) may provide a quantification method for possible (sub)cortical dysfunction occurring prior to or concomitant with motor or cognitive disturbances observed in HD. In this pilot study we construct an automatic classifier distinguishing healthy controls from HD gene carriers using qEEG and derive qEEG features that correlate with clinical markers known to change with disease progression in HD, with the aim of exploring biomarker potential. We included twenty-six HD gene carriers (49.7 ± 8.5 years) and 25 healthy controls (52.7 ± 8.7 years). EEG was recorded for three minutes with subjects at rest. An EEG index was created by applying statistical pattern recognition to a large set of EEG features, which was subsequently tested using 10-fold cross-validation. The index resulted in a continuous variable ranging from 0 to 1: a low value indicating a state close to normal and a high value pointing to HD. qEEG features that correlate specifically with commonly used clinical markers in HD research were derived. The classification index had a specificity of 83%, a sensitivity of 83% and an accuracy of 83%. The area under the curve of the receiver operator characteristic curve was 0.9. qEEG analysis on subsets of electrophysiological features resulted in two highly significant correlations with clinical scores. The results of this pilot study suggest that qEEG may serve as a biomarker in HD. The indices correlating with modalities changing with the progression of the disease may lead to tools based on qEEG that help monitor efficacy in intervention studies.
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