A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography

dc.contributor.authorMoreno, Silvia
dc.contributor.authorQuintero-Parra, Andres
dc.contributor.authorOchoa-Pertuz, Carlos
dc.contributor.authorVillarreal, Reynaldo
dc.contributor.authorKuzmar, Isaac
dc.description.abstractMonitoring of respiration is crucial for determining a patient´s health status, specially previously and after an operation. However, many conventional methods are difficult to use in a spontaneously ventilating patient. This paper presents a method for estimating respiratory rate from the signal of a photoplethysmograph. This is a non-invasive sensor that can be used to obtain an estimation of beats per minute of a given patient by measuring light reflection on the patient’s blood vessel and counting changes in blood flow. The PPG signal also offers information about respiration, so respiratory rate can be obtained through signal processing. The proposed method based on digital filtering was implemented in a wearable device and tested on 30 volunteers, and the results were compared with the ones measured by traditional ways. The results show that there is no statistically significant difference between the data measured by the device and the traditional method.eng
dc.publisherSciencie &Engineering Research Support Society (SERSC)eng
dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.sourceInternational Journal of Signal Processing, Image Processing and Pattern Recognitioneng
dc.sourceVol. 11, No. 2 (2018)spa
dc.subjectBiomedical signal processingeng
dc.subjectRespiratory rateeng
dc.titleA Signal Processing Method for Respiratory Rate Estimation through Photoplethysmographyspa
dcterms.referencesL. Goldman, “Goldman-Cecil medicine”, Philadelphia, PA: Elsevier/Saunders, (2016).eng
dcterms.referencesZ. Sun, “Postoperative Hypoxemia Is Common and Persistent: A Prospective Blinded Observational Study”, Anesth. Analg., vol. 121, no. 3, (2015) September, pp. 709-715.eng
dcterms.referencesL. Nilsson, A. Johansson, and S. Kalman, “Monitoring of respiratory rate in postoperative care using a new photoplethysmographic technique”, PubMed Commons, vol. 16, no. 4, (2001), pp. 309-315.eng
dcterms.referencesN. Patwari, L. Brewer, Q. Tate, O. Kaltiokallio and M. Bocca, “Breathfinding: A Wireless Network That Monitors and Locates Breathing in a Home”, IEEE J. Sel. Top. Signal Process., vol. 8, no. 1, (2014) February, pp. 30-42.eng
dcterms.referencesS. Moreno, A. Quintero, C. Ochoa, M. Bonfante, R. Villareal and J. Pestana, “Remote monitoring system of vital signs for triage and detection of anomalous patient states in the emergency room”, 2016 21st Symp. Signal Process. Images Artif. Vision, STSIVA 2016, (2016), pp. 1-5.eng
dcterms.referencesA. Schäfer and J. Vagedes, “How accurate is pulse rate variability as an estimate of heart rate variability?”, Int. J. Cardiol., vol. 166, no. 1, (2018) January, pp. 15-29.eng
dcterms.referencesL. Nilsson, A. Johansson, J. Svanerudh and S. Kalman, “Is the respiratory component of the photoplethysmographic signal of venous origin?”, Med. Biol. Eng. Comput., vol. 37, (1999), pp. 912- 913.eng
dcterms.referencesP. S. Addison and J. N. Watson, “Secondary wavelet feature decoupling (SWFD) and its use in detecting patient respiration from the photoplethysmogram”, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003, vol. 3, p. 2602-2605.eng
dcterms.referencesY. Der Lin, Y. H. Chien and Y. S. Chen, “Wavelet-based embedded algorithm for respiratory rate estimation from PPG signal”, Biomed. Signal Process. Control, vol. 36, (2017), pp. 138-145.eng
dcterms.referencesK. Nakajima, T. Tamura and H. Miike, “Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique”, Med. Eng. Phys., vol. 18, no. 5, (1996), pp. 365-372.eng
dcterms.referencesS. G. Fleming and L. Tarassenko, “A Comparison of Signal Processing Techniques for the Extraction of Breathing Rate from the Photoplethysmogram”, Int. J. Biol. Life Sci., vol. 2, no. 4, (2006), pp. 233-237.eng
dcterms.referencesY. Zhou, Y. Zheng, C. Wang and J. Yuan, “Extraction of respiratory activity from photoplethysmographic signals based on an independent component analysis technique: Preliminary report”, Instrum. Sci. Technol., vol. 34, no. 5, (2006), pp. 537-545.eng
dcterms.referencesA. Garde, W. Karlen, J. M. Ansermino and G. A. Dumont, “Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram”, PLoS One, vol. 9, no. 1, (2014).eng
dcterms.referencesW. Karlen, S. Raman, J. M. Ansermino and G. A. Dumont, “Multiparameter respiratory rate estimation from the photoplethysmogram”, IEEE Trans. Biomed. Eng., vol. 60, no. 7, (2013), pp. 1946-1953.eng
dcterms.referencesS. A. Shah, S. Fleming, M. Thompson and L. Tarassenko, “Respiratory rate estimation during triage of children in hospitals”, J. Med. Eng. Technol., vol. 39, no. 8, (2015), pp. 514-524.eng
dcterms.referencesK. V. Madhav, E. H. Krishna and K. A. Reddy, “Extraction of respiratory activity from pulse oximeter’s PPG signals using MSICA”, Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, (2016), pp. 823-827.eng
dcterms.referencesD. Birrenkott, M. A. F. Pimentel, P. J. Watkinson and D. A. Clifton, “A Robust Fusion Model for Estimating Respiratory Rate from Photoplethysmography and Electrocardiography”, IEEE Trans. Biomed. Eng., no. c, (2017), pp. 1-9.eng
dcterms.referencesA. Cicone and H. T. Wu, “How nonlinear-type time-frequency analysis can help in sensing instantaneous heart rate and instantaneous respiratory rate from photoplethysmography in a reliable way”, Front. Physiol., vol. 8, no. SEP, (2017), pp. 1-17.eng
dcterms.referencesM. A. F. Pimentel, “Toward a robust estimation of respiratory rate from pulse oximeters”, IEEE Trans. Biomed. Eng., vol. 64, no. 8, (2017), pp. 1914-1923.eng
dcterms.referencesC. Orphanidou, “Derivation of respiration rate from ambulatory ECG and PPG using Ensemble Empirical Mode Decomposition: Comparison and fusion”, Comput. Biol. Med., vol. 81, (2017), pp. 45- 54.eng
dcterms.referencesM. A. Motin, C. K. Karmakar and M. Palaniswami, “An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal”, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2016–October, no. 0, (2016), pp. 3817-3820.eng
dcterms.referencesX. Zhang and Q. Ding, “Fast respiratory rate estimation from PPG signal using sparse signal reconstruction based on orthogonal matching pursuit”, 2016 50th Asilomar Conf. Signals, Syst. Comput., (2016), pp. 1631-5.eng
dcterms.referencesH. Dubey, N. Constant and K. Mankodiya, “RESPIRE: A Spectral Kurtosis-Based Method to Extract Respiration Rate from Wearable PPG Signals”, Proc. - 2017 IEEE 2nd Int. Conf. Connect. Heal. Appl. Syst. Eng. Technol. CHASE 2017, (2017), pp. 84-89.eng
dcterms.referencesElectrical and Computer Engineering in Medicine, “CapnoBase,” 2010. [Online]. Available: http://www.capnobase.org/. [Accessed: 23-Feb-2018].eng


Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
368 B
Item-specific license agreed upon to submission