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Spectral analysis through filter banks aplied to preprocessing oriented to thresholding of pulse oximetry signal

Análisis espectral a través de bancos de filtros aplicado al pre-procesamiento para la umbralización de señales de pulso oximetría

dc.creatorGonzález-Barajas, Javier E.
dc.creatorVelandia, Cristian C.
dc.creatorLyma-Guaqueta, Jeysson
dc.creatorOspina-Fuentes, Pedro
dc.date2016-07-30
dc.date.accessioned2021-03-18T21:02:47Z
dc.date.available2021-03-18T21:02:47Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/48
dc.identifier10.22430/22565337.48
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11382
dc.descriptionThe pulse oximetry signal (SPO2), allows the calculation of the oxygen saturation level in the blood, and it is acquired over the index finger of the patient. Under normal conditions, the variations in SPO2 have correlated with heart rhythm and its maximum value is in phase with the R wave in electrocardiographic signal (ECG). This property enables the SPO2 signal to be the basis for an alternative method for estimating the instantaneous heart rate. For measuring the instantaneous heart rate from the SPO2, it is necessary to carry out a signal thresholding process for detecting peak values in phase with the R-wave of the cardiac complex. In this paper, an iterative solution method is proposed to establish the cutoff frequency selection for the design of digital filters that allow detection of the maximum values of the signal pulse oximetry. The results obtained from the implementation of filter banks, demonstrated their ability to obtain versions of the pulse oximetry signal and frequency values of the spectral components, associated with the maximum values of the SPO2. Experiments used the CAPNOBASE processed signals database, which contains SPO2 and ECG signals, acquired simultaneously. The results allowed to verify that the filter bank allows to select the appropriate version of SPO2 signal with positive peaks, in phase with the R wave of the ECG signal.en-US
dc.descriptionLa señal de pulso oximetría (SPO2) permite el cálculo del porcentaje de oxígeno en sangre y es adquirida en uno de los dedos del paciente. En condiciones normales, las variaciones de la señal SPO2 están correlacionadas con el ritmo cardiaco del paciente y su valor máximo está en fase con la onda R de la señal electrocardiográfica (ECG). Esta propiedad permite a la señal SPO2 ser la base para la estimación de la frecuencia cardiaca instantánea. Con la finalidad de poder medir la frecuencia cardiaca instantánea a partir de la señal SPO2, es necesario un proceso de umbralización para la detección de los valores máximos, en fase con la onda R del complejo cardiaco. En este artículo se presenta un método iterativo para establecer la selección de frecuencias de corte para el diseño de filtros digitales, que permitan la detección de los valores máximos de la señal de pulso oximetría. Se presentan los resultados obtenidos a partir de la implementación de bancos de filtros y se demuestra su capacidad para obtener versiones de la señal de pulso oximetría y los valores de frecuencia de las componentes espectrales asociadas a los valores máximos de las señales de pulso oximetría. Los experimentos elaborados utilizaron señales de la base de datos CAPNOBASE que contienen señales SPO2 y ECG adquiridas simultáneamente. Los datos permitieron comprobar que los bancos de filtros permiten seleccionar la versión adecuada de señal SPO2 con picos positivos en fase con la onda R de la señal ECG.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/48/43
dc.relation/*ref*/L. M. Rodriguez Alvarez, “La pulso oximetría en el ámbito prehospitalario,” Emergencias, vol. 27, no. 1, pp. 9–10, 2015. [2] J. P. S. S. M. López Silva, R. Giannetti, M. L. Dotor, D. Golmayo, P. Martín, F. Miguel-Tobal, A. Bilbao, “Fotopletismografía por transmisión con múltiples diodos láser en el infrarrojo cercano durante el ejercicio físico,” Óptica pura y Apl., vol. 38, no. 1, pp. 31–39, 2005. [3] N. Utami, A. W. Setiawan, H. Zakaria, T. R. Mengko, and R. Mengko, “Extracting blood flow parameters from Photoplethysmograph signals: A review,” in 2013 3rd International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering (ICICI-BME), 2013, vol. 1, pp. 403–407. [4] K. A. Reddy and V. J. Kumar, “Motion Artifact Reduction in Photoplethysmographic Signals using Singular Value Decomposition,” in 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007, 2007, vol. 1, pp. 1–4. [5] J. E. González-Barajas, “Threshold calculation for R wave detection in complex cardiac,” Tecno Lógicas, vol. 17, no. 32, pp. 47–55, Feb. 2014. [6] E. C. Lee, Y. Kim, H. Kim, and J. Kim, “Method for restoring PPG signals using ECG correspondences and SVR,” Electron. Lett., vol. 49, no. 24, pp. 1518–1520, Nov. 2013. [7] Z. Zhang, “Heart rate monitoring from wrist-type photoplethysmographic (PPG) signals during intensive physical exercise,” in 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014, vol. 1, no. 1, pp. 698–702. [8] R. Laulkar and N. Daimiwal, “Acquisition of PPG signal for diagnosis of parameters related to heart,” in 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1), 2012, vol. 1, no. 1, pp. 274–277. [9] A. . C. Banerjee R.; Sinha, “PhotoECG: Photoplethysmographyto estimate ECG parameters,” in Acoustics, Speech and Signal Processing, 2014, vol. 1, no. 1, pp. 4404–4408. [10] N. Daimiwal, M. Sundhararajan, and R. Shriram, “Respiratory rate, heart rate and continuous measurement of BP using PPG,” in 2014 International Conference on Communication and Signal Processing, 2014, vol. 1, no. 1, pp. 999–1002. [11] K. V. Madhav, M. R. Ram, E. H. Krishna, K. N. Reddy, and K. A. Reddy, “A robust signal processing method for extraction of respiratory activity from artifact corrupted PPG signal,” in 2011 IEEE Recent Advances in Intelligent Computational Systems, 2011, vol. 1, no. 1, pp. 451–456. [12] S. Mohamed Yacin, M. Manivannan, and V. Srinivasa Chakravarthy, “Measurement of gastric oscillations from finger photoplethysmographic signal using autoregressive model,” in 2010 International Conference on Communication Control and Computing Technologies, 2010, vol. 1, no. 1, pp. 514–517. [13] R. W. C. G. R. Wijshoff, A. M. T. M. van Asten, W. H. Peeters, R. Bezemer, G. J. Noordergraaf, M. Mischi, and R. M. Aarts, “Photoplethysmography-Based Algorithm for Detection of Cardiogenic Output During Cardiopulmonary Resuscitation,” IEEE Trans. Biomed. Eng., vol. 62, no. 3, pp. 909–921, Mar. 2015. [14] N. V. Manaf and T. Kayikcioglu, “Time difference analysis of two-wavelength photoplethysmograph signals,” in 2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014, vol. 1, no. 1, pp. 2241–2244. [15] M. R. Ram, K. V. Madhav, E. H. Krishna, K. N. Reddy, and K. A. Reddy, “Use of spectral estimation methods for computation of SpO<inf>2</inf> from artifact reduced PPG signals,” in 2011 IEEE Recent Advances in Intelligent Computational Systems, 2011, vol. 1, no. 1, pp. 431–436. [16] K. V. P. Naraharisetti and M. Bawa, “Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal,” in 2011 IEEE International Conference on Electro/Information Technology, 2011, vol. 1, no. 1, pp. 1–8. [17] M. R. Ram, K. V. Madhav, E. H. Krishna, N. R. Komalla, and K. A. Reddy, “A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter,” IEEE Trans. Instrum. Meas., vol. 61, no. 5, pp. 1445–1457, May 2012. [18] C. Karmakar, A. Khandoker, T. Penzel, C. Schobel, and M. Palaniswami, “Detection of Respiratory Arousals Using Photoplethysmography (PPG) Signal in Sleep Apnea Patients,” IEEE J. Biomed. Heal. Informatics, vol. 18, no. 3, pp. 1065–1073, May 2014. [19] L. S. Frida Sandberg, Raquel Bailon, David Hernando, Pablo Laguna, Juan Pablo Martinez, Kristian Solem, “Prediction of intradialytic hypotension using PPG and ECG,” Comput. Cardiol. Conf., vol. 1, no. 1, pp. 1227–1230, 2013. [20] R. P. Arberet S., Lemay M., “Photoplethysmography-based ambulatory heartbeat monitoring embedded into a dedicated bracelet,” Comput. Cardiol. Conf., vol. 1, no. 1, pp. 935–938, 2013. [21] H. S. Shin, C. Lee, and M. Lee, “Adaptive threshold method for the peak detection of photoplethysmographic waveform,” Comput. Biol. Med., vol. 39, no. 12, pp. 1145–1152, Dec. 2009. [22] A. Verma, S. Cabrera, A. Mayorga, and H. Nazeran, “A Robust Algorithm for Derivation of Heart Rate Variability Spectra from ECG and PPG Signals,” in 2013 29th Southern Biomedical Engineering Conference, 2013, vol. 1, no. 1, pp. 35–36. [23] Xiaochuan He, R. A. Goubran, and X. P. Liu, “Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement,” IEEE Trans. Instrum. Meas., vol. 63, no. 6, pp. 1431–1439, Jun. 2014. [24] F. G. Agro D., Canicatti R., Tomasino A., “PPG Embedded System for Blood Pressure Monitoring,” in Annual Conference-From Research to Industry: The Need for a More Effective Technology Transfer (AEIT), 2014, vol. 1, no. 1, pp. 1–6. [25] “CAPNOBASE.” [Online]. Available: http://www.capnobase.org/. [Accessed: 26-Jul-2015]. [26] 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, p. e86427, Jan. 2014. [27] S. R. Oppenheim A., Tratamiento de Señales en Tiempo Discreto, 3rd ed. Pearson Education, 2012.
dc.rightsCopyright (c) 2017 Tecno Lógicasen-US
dc.sourceTecnoLógicas; Vol. 19 No. 37 (2016); 29-43en-US
dc.sourceTecnoLógicas; Vol. 19 Núm. 37 (2016); 29-43es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectPulse oximetryen-US
dc.subjectfilter banken-US
dc.subjectspectral analysisen-US
dc.subjectheart rate.en-US
dc.subjectPulso oximetríaes-ES
dc.subjectbanco de filtroses-ES
dc.subjectanálisis espectrales-ES
dc.subjectfrecuencia cardiacaes-ES
dc.titleSpectral analysis through filter banks aplied to preprocessing oriented to thresholding of pulse oximetry signalen-US
dc.titleAnálisis espectral a través de bancos de filtros aplicado al pre-procesamiento para la umbralización de señales de pulso oximetríaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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