Characterization of cardiac and respiratory system of healthy subjects in supine and sitting position
IBRPIA2019_Article_BG.pdf (431,1Kb) (Restricted access) Request copy
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Document typePart of book or chapter of book
Rights accessRestricted access - publisher's policy
Studies based on the cardiac and respiratory system have allowed a better knowledge of their behavior to contribute with the diagnosis and treatment of diseases associated with them. The main goal of this project was to analyze the behavior of the cardiorespiratory system in healthy subjects, depending on the body position. The electrocardiography and respiratory flow signals were recorded in two positions, supine and sitting. Each signal was analyzed considering sliding windows of 30 s, with and overlapping of 50%. Temporal and spectral features were extracted from each signal. A total of 187 features were extracted for each window. According to statistical analysis, 148 features showed significant differences when comparing the position of the subject. Afterwards, the classifications methods based on decision trees, k-nearest neighbor and support vector machines were applied to identify the best classification model. The most advantageous performance model was obtained with a linear support vector machine method, with an accuracy of 99.5%, a sensitivity of 99.2% and a specificity of 99.6%. In conclusion, we have observed that the position of the body (supine or sitting) could modulate the cardiac and respiratory system response. New statistical models might provide new tools to analyze the behavior of these systems and the cardiorespiratory interaction complexity
CitationGiraldo, B. Characterization of cardiac and respiratory system of healthy subjects in supine and sitting position. A: "Pattern recognition and image analysis". Berlín: Springer, 2019, p. 367-377.