Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/NU20-04-00294" target="_blank" >NU20-04-00294: Diagnostika onemocnění s Lewyho tělísky v prodromálním stadiu založená na analýze multimodálních dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

Brno, Czech Republic

Místo konání akce

Brno

Datum konání akce

27. 4. 2021

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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