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Abstract
The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.
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Authors (7)
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Beata Graff
- Department of Hypertension and Diabetology, Medical University of Gdansk, 80‑210 Gdańsk, Poland
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Krzysztof Narkiewicz
- Department of Hypertension and Diabetology, Medical University of Gdansk, 80‑210 Gdańsk, Poland
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Cognitive Computation
no. 14,
pages 2120 - 2140,
ISSN: 1866-9956 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Szczuko P., Kurowski A., Odya P., Czyżewski A., Kostek B., Graff B., Narkiewicz K.: Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction// Cognitive Computation -Vol. 14,iss. 6 (2022), s.2120-2140
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/s12559-021-09908-8
- Sources of funding:
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- IDUB Gdańsk University of Technology within the Curium-Combating Coronavirus program implemented under the “Initiative of Excellence-Research University” (No. 034427.SARS).
- Verified by:
- Gdańsk University of Technology
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