000150075 001__ 150075
000150075 005__ 20250130182920.0
000150075 0247_ $$2doi$$a10.1155/2013/631978
000150075 0248_ $$2sideral$$a83389
000150075 037__ $$aART-2013-83389
000150075 041__ $$aeng
000150075 100__ $$0(orcid)0000-0002-0690-3193$$aPeláez-Coca, M.D.
000150075 245__ $$aCross Time-Frequency Analysis for combining information of several sources. Application to estimation of Spontaneous Respiratory Rate from Photoplethysmography
000150075 260__ $$c2013
000150075 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150075 5203_ $$aA methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary
biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration.
000150075 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000150075 590__ $$a1.018$$b2013
000150075 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b39 / 51 = 0.765$$c2013$$dQ4$$eT3
000150075 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150075 700__ $$aOrini, M.
000150075 700__ $$0(orcid)0000-0001-8742-0072$$aLazaro, J.$$uUniversidad de Zaragoza
000150075 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, R.$$uUniversidad de Zaragoza
000150075 700__ $$0(orcid)0000-0001-7285-0715$$aGil, E.$$uUniversidad de Zaragoza
000150075 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000150075 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000150075 773__ $$g2013 (2013), 631978 [8 pp.]$$pCOMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE$$tCOMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE$$x1748-670X
000150075 8564_ $$s972199$$uhttps://zaguan.unizar.es/record/150075/files/texto_completo.pdf$$yVersión publicada
000150075 8564_ $$s2479515$$uhttps://zaguan.unizar.es/record/150075/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150075 909CO $$ooai:zaguan.unizar.es:150075$$particulos$$pdriver
000150075 951__ $$a2025-01-30-16:18:39
000150075 980__ $$aARTICLE