Detail publikačního výsledku

Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy

MIKULEC, M.; GALÁŽ, Z.; MEKYSKA, J.; MUCHA, J.; BRABENEC, L.; MORÁVKOVÁ, I.; REKTOROVÁ, I.

Originální název

Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy

Anglický název

Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

This paper is devoted to the computerized automated diagnosis of the prodromal state of Lewy body diseases (LBD) based on actigraphy. LBD is a group of neurodegenerative diseases that require early treatment to alleviate the course of the disease and improve the quality of the lives of patients. This work proposes a method of prodromal diagnosis of LBD based on quantitative analysis of actigraphic sleep data. A new method of sleep and wake detection based on the XGBoost classifier and the angle of the z-axis is introduced, which achieves 83% accuracy and surpasses the results of state-of-the-art methods. Furthermore, a method that can distinguish subjects with prodromal LBD (50 subjects with Parkinson's disease, dementia with Lewy bodies or mild cognitive impairment) and healthy controls (63 subjects) with 94% accuracy was introduced. The sensitivity of the method of 100% and specificity of 91% was considered sufficient for clinical practice and the proposed methods can help develop decision-making tools that maximize the potential for an early and objective diagnosis of LBD.

Anglický abstrakt

This paper is devoted to the computerized automated diagnosis of the prodromal state of Lewy body diseases (LBD) based on actigraphy. LBD is a group of neurodegenerative diseases that require early treatment to alleviate the course of the disease and improve the quality of the lives of patients. This work proposes a method of prodromal diagnosis of LBD based on quantitative analysis of actigraphic sleep data. A new method of sleep and wake detection based on the XGBoost classifier and the angle of the z-axis is introduced, which achieves 83% accuracy and surpasses the results of state-of-the-art methods. Furthermore, a method that can distinguish subjects with prodromal LBD (50 subjects with Parkinson's disease, dementia with Lewy bodies or mild cognitive impairment) and healthy controls (63 subjects) with 94% accuracy was introduced. The sensitivity of the method of 100% and specificity of 91% was considered sufficient for clinical practice and the proposed methods can help develop decision-making tools that maximize the potential for an early and objective diagnosis of LBD.

Klíčová slova

actigraphy; machine learning; neurodegenerative diseases; Lewy body diseases; RBD; SHAP values; sleep diary; XGBoost

Klíčová slova v angličtině

actigraphy; machine learning; neurodegenerative diseases; Lewy body diseases; RBD; SHAP values; sleep diary; XGBoost

Autoři

MIKULEC, M.; GALÁŽ, Z.; MEKYSKA, J.; MUCHA, J.; BRABENEC, L.; MORÁVKOVÁ, I.; REKTOROVÁ, I.

Vydáno

18.08.2022

Nakladatel

IEEE

ISBN

978-1-6654-6948-7

Kniha

2022 45th International Conference on Telecommunications and Signal Processing (TSP)

Strany od

403

Strany do

406

Strany počet

4

URL

https://ieeexplore.ieee.org/document/9851316

BibTex

@inproceedings{BUT178287,
  author="MIKULEC, M. and GALÁŽ, Z. and MEKYSKA, J. and MUCHA, J. and BRABENEC, L. and MORÁVKOVÁ, I. and REKTOROVÁ, I.",
  title="Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy",
  booktitle="2022 45th International Conference on Telecommunications and Signal Processing (TSP)",
  year="2022",
  pages="403--406",
  publisher="IEEE",
  doi="10.1109/TSP55681.2022.9851316",
  isbn="978-1-6654-6948-7",
  url="https://ieeexplore.ieee.org/document/9851316"
}