A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography
Monitoring 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...
- Autores:
-
Moreno, Silvia
Quintero-Parra, Andres
Ochoa-Pertuz, Carlos
Villarreal, Reynaldo
Kuzmar, Isaac
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/2342
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/2342
- Palabra clave:
- Biomedical signal processing
Photoplethysmography
Telemedicine
Respiratory rate
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| title |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| spellingShingle |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography Biomedical signal processing Photoplethysmography Telemedicine Respiratory rate |
| title_short |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| title_full |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| title_fullStr |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| title_full_unstemmed |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| title_sort |
A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography |
| dc.creator.fl_str_mv |
Moreno, Silvia Quintero-Parra, Andres Ochoa-Pertuz, Carlos Villarreal, Reynaldo Kuzmar, Isaac |
| dc.contributor.author.none.fl_str_mv |
Moreno, Silvia Quintero-Parra, Andres Ochoa-Pertuz, Carlos Villarreal, Reynaldo Kuzmar, Isaac |
| dc.subject.eng.fl_str_mv |
Biomedical signal processing Photoplethysmography Telemedicine Respiratory rate |
| topic |
Biomedical signal processing Photoplethysmography Telemedicine Respiratory rate |
| description |
Monitoring 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. |
| publishDate |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2018-11-09T19:29:49Z |
| dc.date.available.none.fl_str_mv |
2018-11-09T19:29:49Z |
| dc.date.issued.none.fl_str_mv |
2018-02 |
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article |
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http://purl.org/coar/resource_type/c_6501 |
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20054254 |
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http://hdl.handle.net/20.500.12442/2342 |
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20054254 |
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http://hdl.handle.net/20.500.12442/2342 |
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eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| dc.publisher.eng.fl_str_mv |
Sciencie &Engineering Research Support Society (SERSC) |
| dc.source.eng.fl_str_mv |
International Journal of Signal Processing, Image Processing and Pattern Recognition |
| dc.source.spa.fl_str_mv |
Vol. 11, No. 2 (2018) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.eng.fl_str_mv |
https://www.researchgate.net/publication/324843101_A_Signal_Processing_Method_for_Respiratory_Rate_Estimation_through_Photoplethysmography |
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1814076168895201280 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Moreno, Silvia93dbe641-5f99-4a7d-bfd9-faa0ea07e8dd-1Quintero-Parra, Andres07c89137-b06f-4923-bc86-d2072e125bef-1Ochoa-Pertuz, Carlosf67d371f-db25-44ce-8240-7ab75c3aafb2-1Villarreal, Reynaldo342430b4-b933-4003-bd80-2c8f9f588a13-1Kuzmar, Isaac532c55ce-6c8d-4a73-b7b6-77619a586ed1-12018-11-09T19:29:49Z2018-11-09T19:29:49Z2018-0220054254http://hdl.handle.net/20.500.12442/2342Monitoring 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.engSciencie &Engineering Research Support Society (SERSC)International Journal of Signal Processing, Image Processing and Pattern RecognitionVol. 11, No. 2 (2018)https://www.researchgate.net/publication/324843101_A_Signal_Processing_Method_for_Respiratory_Rate_Estimation_through_PhotoplethysmographyBiomedical signal processingPhotoplethysmographyTelemedicineRespiratory rateA Signal Processing Method for Respiratory Rate Estimation through Photoplethysmographyarticlehttp://purl.org/coar/resource_type/c_6501L. Goldman, “Goldman-Cecil medicine”, Philadelphia, PA: Elsevier/Saunders, (2016).Z. Sun, “Postoperative Hypoxemia Is Common and Persistent: A Prospective Blinded Observational Study”, Anesth. Analg., vol. 121, no. 3, (2015) September, pp. 709-715.L. 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.N. 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.S. 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.A. 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.L. 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.P. 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.Y. 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.K. 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.S. 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.Y. 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.A. 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).W. 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.S. 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.K. 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.D. 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.A. 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.M. 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.C. 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.M. 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.X. 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.H. 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.Electrical and Computer Engineering in Medicine, “CapnoBase,” 2010. [Online]. Available: http://www.capnobase.org/. 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