EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBD Article Swipe
El Jeong
,
Yong Woo Shin
,
Jung‐Ick Byun
,
Jun‐Sang Sunwoo
,
Monica Roascio
,
Pietro Mattioli
,
Laura Giorgetti
,
Francesco Famà
,
Gabriele Arnulfo
,
Dario Arnaldi
,
Han‐Joon Kim
,
Ki‐Young Jung
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.09.04.23294964
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.09.04.23294964
Background Idiopathic/Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson’s disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. Objectives In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. Methods At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index and Shannon entropy. Three models were used for survival prediction, and four models were used for α-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. Results A total of 236 iRBD patients were followed-up for up to eight years (mean 3.5 years), and 31 patients converted to α-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. EEG slowing was an important feature for both models. Conclusions Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.
Related Topics
Concepts
Synucleinopathies
Electroencephalography
Dementia with Lewy bodies
Parkinson's disease
REM sleep behavior disorder
Brier score
Random forest
Receiver operating characteristic
Artificial intelligence
Medicine
Disease
Computer science
Psychology
Polysomnography
Dementia
Internal medicine
Neuroscience
Alpha-synuclein
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.09.04.23294964
- https://www.medrxiv.org/content/medrxiv/early/2023/09/05/2023.09.04.23294964.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386448894
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386448894Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.09.04.23294964Digital Object Identifier
- Title
-
EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBDWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-05Full publication date if available
- Authors
-
El Jeong, Yong Woo Shin, Jung‐Ick Byun, Jun‐Sang Sunwoo, Monica Roascio, Pietro Mattioli, Laura Giorgetti, Francesco Famà, Gabriele Arnulfo, Dario Arnaldi, Han‐Joon Kim, Ki‐Young JungList of authors in order
- Landing page
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https://doi.org/10.1101/2023.09.04.23294964Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2023/09/05/2023.09.04.23294964.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2023/09/05/2023.09.04.23294964.full.pdfDirect OA link when available
- Concepts
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Synucleinopathies, Electroencephalography, Dementia with Lewy bodies, Parkinson's disease, REM sleep behavior disorder, Brier score, Random forest, Receiver operating characteristic, Artificial intelligence, Medicine, Disease, Computer science, Psychology, Polysomnography, Dementia, Internal medicine, Neuroscience, Alpha-synucleinTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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