Publication detail

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

MIKULEC, M. MEKYSKA, J. GÁLÁŽ Z.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

actigraphy, machine learning, Parkinson’s disease, SHAP values, sleep diaries, sleep disorders, XGBoost

Authors

MIKULEC, M.; MEKYSKA, J.; GÁLÁŽ Z.

Released

26. 4. 2022

Publisher

Brno University of Technology, Faculty of Electronic Engineering and Communication

Location

Brno, Czech Republic

ISBN

978-80-214-6030-0

Book

Proceedings II of the 28th Conference STUDENT EEICT 2022 Selected papers

Edition

1

Pages from

281

Pages to

285

Pages count

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3_DOI.pdf

BibTex

@inproceedings{BUT177646,
  author="Marek {Mikulec} and Jiří {Mekyska} and Zoltán {Galáž}",
  title="Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries",
  booktitle="Proceedings II of the 28th Conference STUDENT EEICT 2022 Selected papers",
  year="2022",
  series="1",
  pages="281--285",
  publisher="Brno University of Technology, Faculty of Electronic Engineering and Communication",
  address="Brno, Czech Republic",
  isbn="978-80-214-6030-0",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3_DOI.pdf"
}