Spirometer and sEMG respiratory patterns for clinical decision support system

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work con...

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Bibliographic Details
Main Authors: Kumar, Amit Krishan, Assaf, Mansour H., Groza, Voicu Z., Petriu, Emil M.
Format: Conference Proceeding
Language:English
Subjects:
4D extrapolation
Biological system modeling
Clinical decision support system
Computational modeling
Decision making
Decision support systems
Fatigue
respiratory patterns
Sensors
spirometer
surface electromyography
Surface impedance
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Summary:To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.
ISSN:2642-2077
DOI:10.1109/I2MTC53148.2023.10175925