Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy

D 2022

Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy

MIKULEC, Marek, Zoltan GALAZ, Jiri MEKYSKA, Jan MUCHA, Luboš BRABENEC et. al.

Základní údaje

Originální název

Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy

Autoři

MIKULEC, Marek, Zoltan GALAZ, Jiri MEKYSKA, Jan MUCHA, Luboš BRABENEC (203 Česká republika, domácí), Ivona MORÁVKOVÁ (703 Slovensko, domácí) a Irena REKTOROVÁ (203 Česká republika, garant, domácí)

Vydání

NEW YORK, 2022 45th International Conference on Telecommunications and Signal Processing (TSP), od s. 403-406, 4 s. 2022

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14740/22:00134704

Organizační jednotka

Středoevropský technologický institut

ISBN

978-1-6654-6948-7

DOI

http://dx.doi.org/10.1109/TSP55681.2022.9851316

UT WoS

001070846300082

Klíčová slova anglicky

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

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 5. 4. 2024 10:57, Mgr. Eva Dubská

Anotace

V originále

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 pro-dromal 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.

Návaznosti

NU20-04-00294, projekt VaV

Název: Diagnostika onemocnění s Lewyho tělísky v prodromálním stadiu založená na analýze multimodálních dat

Investor: Ministerstvo zdravotnictví ČR, Diagnostika onemocnění s Lewyho tělísky v prodromálním stadiu založená na analýze multimodálních dat

Citovat

MIKULEC, Marek, Zoltan GALAZ, Jiri MEKYSKA, Jan MUCHA, Luboš BRABENEC, Ivona MORÁVKOVÁ a Irena REKTOROVÁ. Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy. Online. In 2022 45th International Conference on Telecommunications and Signal Processing (TSP). NEW YORK: IEEE, 2022, s. 403-406. ISBN 978-1-6654-6948-7. Dostupné z: https://dx.doi.org/10.1109/TSP55681.2022.9851316.

@inproceedings{2370277,
   author = {Mikulec, Marek and Galaz, Zoltan and Mekyska, Jiri and Mucha, Jan and Brabenec, Luboš and Morávková, Ivona and Rektorová, Irena},
   address = {NEW YORK},
   booktitle = {2022 45th International Conference on Telecommunications and Signal Processing (TSP)},
   doi = {http://dx.doi.org/10.1109/TSP55681.2022.9851316},
   keywords = {actigraphy; machine learning; neurodegenerative diseases; Lewy body diseases; RBD; SHAP values; sleep diary; XGBoost},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {NEW YORK},
   isbn = {978-1-6654-6948-7},
   pages = {403-406},
   publisher = {IEEE},
   title = {Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy},
   url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9851316},
   year = {2022}
}

TY  - JOUR
ID  - 2370277
AU  - Mikulec, Marek - Galaz, Zoltan - Mekyska, Jiri - Mucha, Jan - Brabenec, Luboš - Morávková, Ivona - Rektorová, Irena
PY  - 2022
TI  - Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy
PB  - IEEE
CY  - NEW YORK
SN  - 9781665469487
KW  - actigraphy
KW  - machine learning
KW  - neurodegenerative diseases
KW  - Lewy body diseases
KW  - RBD
KW  - SHAP values
KW  - sleep diary
KW  - XGBoost
UR  - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9851316
N2  - 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 pro-dromal 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.
ER  -

MIKULEC, Marek, Zoltan GALAZ, Jiri MEKYSKA, Jan MUCHA, Luboš BRABENEC, Ivona MORÁVKOVÁ a Irena REKTOROVÁ. Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy. Online. In \textit{2022 45th International Conference on Telecommunications and Signal Processing (TSP)}. NEW YORK: IEEE, 2022, s.~403-406. ISBN~978-1-6654-6948-7. Dostupné z: https://dx.doi.org/10.1109/TSP55681.2022.9851316.

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