Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144352" target="_blank" >RIV/00216305:26220/22:PU144352 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries
Original language description
Parkinson’s disease is accompanied by sleep disorders in most cases. Therefore patients with Parkinson’s disease could be identified according to proper sleep metrics. The study aims to train a classifier and identify proper sleep metrics, that could distinguish patients with Parkinson’s disease from subjects in control group based on data from actigraphy and sleep diaries. Study sample consisted of 23 patients with probable Parkinson’s disease and 71 control subjects resulting in 654 nights of actigraphy and sleep diary data, with 26 unique features per night. XGBoost classifier was trained to distinguish the groups, scoring 80% accuracy and 52% F1 on test data. Actigraphy based parameters targeted on wake analysis during sleep were marked as most important. The study provided classifier and obtained the most important parameters to identify patients with Parkinson’s disease based on actigraphy and sleep diary data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/NU20-04-00294" target="_blank" >NU20-04-00294: Diagnostics of Lewy body diseases in prodromal stage based on multimodal data analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings II of the 28th Conference STUDENT EEICT 2022
ISBN
978-80-214-6030-0
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
Neuveden
Place of publication
Brno, Czech Republic
Event location
Brno
Event date
Apr 27, 2021
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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