Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | 2022 45th International Conference on Telecommunications and Signal Processing (TSP) |
| MU Faculty or unit | |
| Citation | |
| web | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9851316 |
| 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 |
| Description | 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. |
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