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Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

Abstrakt

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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Wersja publikacji
Accepted albo Published Version
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Cognitive Computation strony 1 - 21,
ISSN: 1866-9956
Język:
angielski
Rok wydania:
2021
Opis bibliograficzny:
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 -, (2021), s.1-21
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s12559-021-09908-8
Źródła finansowania:
  • IDUB Gdańsk University of Technology within the Curium-Combating Coronavirus program implemented under the “Initiative of Excellence-Research University” (No. 034427.SARS).
Weryfikacja:
Politechnika Gdańska

wyświetlono 36 razy

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