Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy
- Resource Type
- Conference
- Authors
- Mikulec, Marek; Galaz, Zoltan; Mekyska, Jiri; Mucha, Jan; Brabenec, Lubos; Moravkova, Ivona; Rektorova, Irena
- Source
- 2022 45th International Conference on Telecommunications and Signal Processing (TSP) Telecommunications and Signal Processing (TSP), 2022 45th International Conference on. :403-406 Jul, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Sensitivity
Statistical analysis
Parkinson's disease
Decision making
Telecommunications
Recording
Diseases
actigraphy
machine learning
neurodegenerative diseases
Lewy body diseases
RBD
SHAP values
sleep diary
XGBoost
- Language
This paper is devoted to the computerized auto-mated 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.