Advances in Photopletysmography Signal Analysis for Biomedical Applications

Jermana L Moraes, Matheus X Rocha, Glauber G Vasconcelos, José E Vasconcelos Filho, Victor Hugo C de Albuquerque, Auzuir R Alexandria, Jermana L Moraes, Matheus X Rocha, Glauber G Vasconcelos, José E Vasconcelos Filho, Victor Hugo C de Albuquerque, Auzuir R Alexandria

Abstract

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost⁻benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

Keywords: Internet of Health Things; cardiovascular diseases; health care; heart rate variability; photoplethysmography.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Mortality rates by NTCD per 100,000 habitants, all ages, for region of WHO, 2012 [9].
Figure 2
Figure 2
Comparative of 20 years (1997–2017) of PPG publications. Data were obtained from Web of Science TM using “photoplethysmography” as topic (accessed on 20 February 2018).
Figure 3
Figure 3
PPG signal analysis.
Figure 4
Figure 4
Different measurement points of PTT [45].
Figure 5
Figure 5
Representation of the operation of photoplethysmography sensors for finger application, by transmission (a) and by reflection (b). Adapted from [58].
Figure 6
Figure 6
Working principle of PPG sensors [19].
Figure 7
Figure 7
PPG instrumentation.

References

    1. Paschoal M., Gonçalves N., Petrelluzzi K., Machado R. Controle autonômico cardíaco durante a execução de atividade física dinâmica de baixa intensidade. Rev. Soc. Cardiol. Estado de São Paulo. 2003;13:S1–S11.
    1. Lin C.H. Assessment of bilateral photoplethysmography for lower limb peripheral vascular occlusive disease using color relation analysis classifier. Comput. Methods Progr. Biomed. 2011;103:121–131. doi: 10.1016/j.cmpb.2010.06.014.
    1. Faurholt-Jepsen M., Kessing L.V., Munkholm K. Heart rate variability in bipolar disorder: A systematic review and meta-analysis. Neurosci. Biobehav. Rev. 2017;73:68–80. doi: 10.1016/j.neubiorev.2016.12.007.
    1. Palma J.A., Benarroch E.E. Neural control of the heart. Neurology. 2014;83:261–271. doi: 10.1212/WNL.0000000000000605.
    1. Aubert A.E., Seps B., Beckers F. Heart rate variability in athletes. Sports Med. 2003;33:889–919. doi: 10.2165/00007256-200333120-00003.
    1. Florea V.G., Cohn J.N. The Autonomic Nervous System and Heart Failure. Circ. Res. 2014;114:1815–1826. doi: 10.1161/CIRCRESAHA.114.302589.
    1. Rajendra A.U., Paul J.K., Natarajan K., Min L.C., Suri J.S. Heart rate variability: A review. Med. Biol. Eng. Comput. 2006;44:1031–1051. doi: 10.1007/s11517-006-0119-0.
    1. Porto L.G.G., Junqueira L.F., Jr. Comparison of Time-Domain Short-Term Heart Interval Variability Analysis Using a Wrist-Worn Heart Rate Monitor and the Conventional Electrocardiogram. Pacing Clin. Electrophysiol. 2009;32:43–51. doi: 10.1111/j.1540-8159.2009.02175.x.
    1. Mendis S. Global Status Report on Noncommunicable Diseases 2014. World Health Organization; Geneva, Switzerland: 2014. p. 298.
    1. De Alexandria A.R., Cortez P.C., Bessa J.A., da Silva Félix J.H., de Abreu J.S., de Albuquerque V.H.C. Psnakes: A new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images. Comput. Methods Progr. Biomed. 2014;116:260–273. doi: 10.1016/j.cmpb.2014.05.009.
    1. Corrêa L.A.F. Ph.D. Thesis. Programa de Pós Graduação em Engenharia Biomédica, Universidade Federal do Rio de Janeiro, UFRJ; Rio de Janeiro, Brasil: 2006. Sistema não Invasivo de Monitorização da Pressão Arterial e da Onda de Pulso Utilizando a Fotopletismografia.
    1. Kilsztajn S., Rossbach A., Câmara M., Carmo M. Health services, expenses and aging of the Brazilian population. Rev. Bras. Study Popul. 2003;20:93–108.
    1. Fan F., Yan Y., Tang Y., Zhang H. A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion. Comput. Biol. Med. 2017;91:291–305. doi: 10.1016/j.compbiomed.2017.10.017.
    1. Birrenkott D., Pimentel M.A., Watkinson P.J., Clifton D.A. A robust fusion model for estimating respiratory rate from photoplethysmography and electrocardiography. IEEE Trans. Biomed. Eng. 2017 doi: 10.1109/TBME.2017.2778265.
    1. Akay M. Wiley Encyclopedia of Biomedical Engineering. Wiley-Interscience; Hoboken, NJ, USA: 2006. p. 4152.
    1. Papini G., Fonseca P., Aubert X., Overeem S., Bergmans J., Vullings R. Photoplethysmography beat detection and pulse morphology quality assessment for signal reliability estimation; Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Seogwipo, Korea. 11–15 July 2017; pp. 117–120.
    1. Da S., Luz E.J., Nunes T.M., de Albuquerque V.H.C., Papa J.P., Menotti D. ECG arrhythmia classification based on optimum-path forest. Expert Syst. Appl. 2013;40:3561–3573.
    1. De Albuquerque V.H.C., Nunes T.M., Pereira D.R., Luz E.J.D.S., Menotti D., Papa J.P., Tavares J.M.R.S. Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput. Appl. 2018;29:679–693. doi: 10.1007/s00521-016-2472-8.
    1. Zhao D., Sun Y., Wan S., Wang F. SFST: A robust framework for heart rate monitoring from photoplethysmography signals during physical activities. Biomed. Signal Process. Control. 2017;33:316–324. doi: 10.1016/j.bspc.2016.12.005.
    1. Pradhan N., Rajan S., Adler A., Redpath C. Classification of the quality of wristband-based photoplethysmography signals; Proceedings of the 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA); Rochester, MN, USA. 7–10 May 2017; pp. 269–274.
    1. Hussein A.F., Kumar A., Burbano-Fernandez M., Ramirez-Gonzalez G., Abdulhay E., de Albuquerque V.H.C. An Automated Remote Cloud-Based Heart Rate Variability Monitoring System. IEEE Access. 2018 doi: 10.1109/ACCESS.2018.2831209.
    1. Hassan M., Malik A., Fofi D., Saad N., Karasfi B., Ali Y., Meriaudeau F. Heart rate estimation using facial video: A review. Biomed. Signal Process. Control. 2017;38:346–360. doi: 10.1016/j.bspc.2017.07.004.
    1. Charlton P., Birrenkott D.A., Bonnici T., Pimentel M.A.F., Johnson A.E.W., Alastruey J., Tarassenko L., Watkinson P.J., Beale R., Clifton D.A. Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram: A Review. IEEE Rev. Biomed. Eng. 2018 doi: 10.1109/RBME.2017.2763681.
    1. Alian A.A., Shelley K.H. Photoplethysmography. Best Pract. Res. Clin. Anaesthesiol. 2014;28:395–406. doi: 10.1016/j.bpa.2014.08.006.
    1. Sun Y., Thakor N. Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging. IEEE Trans. Biomed. Eng. 2016;63:463–477. doi: 10.1109/TBME.2015.2476337.
    1. Pfeifer G., Garfinkel S.N., Praag C.D.G.V., Sahota K., Betka S., Critchley H.D. Feedback from the heart: Emotional learning and memory is controlled by cardiac cycle, interoceptive accuracy and personality. Biol. Psychol. 2017;126:19–29. doi: 10.1016/j.biopsycho.2017.04.001.
    1. Rodrigues K.A.S., Pereira M.H.R., Pádua F.L.C. Detecção em tempo real da frequência cardíaca de pessoas por meio da análise de variações temporais em vídeos. E-xacta. 2016;9:49–62. doi: 10.18674/exacta.v9i1.1666.
    1. Choi S., Min K., Kim N.N., Munarriz R., Goldstein I., Traish A.M. Laser Oximetry: A Novel Noninvasive Method to Determine Changes in Penile Hemodynamics in an Anesthetized Rabbit Model. J. Androl. 2002;23:278–283.
    1. Schwarz L. Ph.D. Thesis. Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Santa Catarina, UFSC; Santa Catarina, Brasil: 2007. Proposta de um Sistema Telemétrico Para Aquisição de Sinais Fisiológicos.
    1. Peter L., Vorek I., Massot B., Bryjova I., Urbanczyk T. Determination of Blood Vessels Expandability; Multichannel Photoplethysmography. IFAC–PapersOnLine. 2016;49:284–288. doi: 10.1016/j.ifacol.2016.12.048.
    1. Madhavan G. Plethysmography. Biomed. Instrum. Technol. 2005;39:367–371. doi: 10.2345/0899-8205(2005)39[367:P];2.
    1. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 2007;28:R1–R39. doi: 10.1088/0967-3334/28/3/R01.
    1. Fan Q., Li K. Non-contact remote estimation of cardiovascular parameters. Biomed. Signal Process. Control. 2018;40:192–203. doi: 10.1016/j.bspc.2017.09.022.
    1. Bhattacharya J., Kanjilal P., Muralidhar V. Analysis and characterization of photo-plethysmographic signal. IEEE Trans. Biomed. Eng. 2001;48:5–11. doi: 10.1109/10.900243.
    1. Meredith D., Clifton D., Charlton P., Brooks J., Pugh C., Tarassenko L. Photoplethysmographic derivation of respiratory rate: A review of relevant physiology. J. Med. Eng. Technol. 2012;36:1–7. doi: 10.3109/03091902.2011.638965.
    1. Moyle J.T.B. Pulse Oximetry. 2nd ed. BMJ; London, UK: 2002.
    1. Hejjel L., Gál I. Heart rate variability analysis. Acta Physiologica Hungarica. 2001;88:219–230. doi: 10.1556/APhysiol.88.2001.3-4.4.
    1. Hemon M.C., Phillips J.P. Comparison of foot finding methods for deriving instantaneous pulse rates from photoplethysmographic signals. J. Clin. Monit. Comput. 2016;30:157–168. doi: 10.1007/s10877-015-9695-6.
    1. Phillips J.P., Kyriacou P.A. Comparison of methods for determining pulse arrival time from Doppler and photoplethysmography signals; Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Chicago, IL, USA. 26–30 August 2014; pp. 3809–3812.
    1. Millasseau S.C., Stewart A.D., Patel S.J., Redwood S.R., Chowienczyk P.J. Evaluation of Carotid-Femoral Pulse Wave Velocity. Hypertension. 2005;45:222–226. doi: 10.1161/01.HYP.0000154229.97341.d2.
    1. Vardoulis O., Papaioannou T.G., Stergiopulos N. Validation of a novel and existing algorithms for the estimation of pulse transit time: Advancing the accuracy in pulse wave velocity measurement. Am. J. Physiol. Heart Circ. Physiol. 2013;304:H1558–H1567. doi: 10.1152/ajpheart.00963.2012.
    1. Gesche H., Grosskurth D., Küchler G., Patzak A. Continuous blood pressure measurement by using the pulse transit time: Comparison to a cuff-based method. Eur. J. Appl. Physiol. 2012;112:309–315. doi: 10.1007/s00421-011-1983-3.
    1. Hennig A., Patzak A. Continuous blood pressure measurement using pulse transit time. Somnol. Schlafforschung Schlafmed. 2013;17:104–110. doi: 10.1007/s11818-013-0617-x.
    1. Zheng Y.L., Yan B.P., Zhang Y.T., Poon C.C.Y. An Armband Wearable Device for Overnight and Cuff-Less Blood Pressure Measurement. IEEE Trans. Biomed. Eng. 2014;61:2179–2186. doi: 10.1109/TBME.2014.2318779.
    1. Choi Y., Zhang Q., Ko S. Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert–Huang transform. Comput. Electr. Eng. 2013;39:103–111. doi: 10.1016/j.compeleceng.2012.09.005.
    1. Yang C., Tavassolian N. Pulse transit time measurement using seismocardiogram, photoplethysmogram, and acoustic recordings: Evaluation and comparison. IEEE J. Biomed. Health Inf. 2017;22:733–740. doi: 10.1109/JBHI.2017.2696703.
    1. Mukkamala R., Hahn J.O. Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits. IEEE Trans. Biomed. Eng. 2018;65:1410–1420. doi: 10.1109/TBME.2017.2756018.
    1. Davies J.I., Struthers A.D. Pulse wave analysis and pulse wave velocity: A critical review of their strengths and weaknesses. J. Hypertens. 2003;21:463–472. doi: 10.1097/00004872-200303000-00004.
    1. Nabeel P.M., Jayaraj J., Mohanasankar S. Single-source PPG-based local pulse wave velocity measurement: a potential cuffless blood pressure estimation technique. Physiol. Meas. 2017;12:2122–2140. doi: 10.1088/1361-6579/aa9550.
    1. Borik S., Cap I. Measurement and Analysis Possibilities of Pulse Wave Signals. Adv. Electr. Electron. Eng. 2013;11 doi: 10.15598/aeee.v11i6.759.
    1. Chobanian A.V., Bakris G.L., Black H.R., Cushman W.C., Green L.A., Izzo J.L., Jr., Jones D.W., Materson B.J., Oparil S., Wright J.T., Jr., et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: The JNC 7 report. JAMA. 2003;289:2560–2571. doi: 10.1001/jama.289.19.2560.
    1. Alvim R.D.O., Santos P.C.J.L., Bortolotto L.A., Mill J.A.G., Pereira A.D.C. Arterial Stiffness: Pathophysiological and Genetic Aspects. Int. J. Cardiovasc. Sci. 2017;30:433–441. doi: 10.5935/2359-4802.20170053.
    1. Pereira T., Maldonado J., Pereira L., Conde J. Aortic stiffness is an independent predictor of stroke in hypertensive patients. Arq. Bras. Cardiol. 2013;100:437–443. doi: 10.5935/abc.20130079.
    1. McCombie D.B., Reisner A.T., Asada H.H. Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics; Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society; New York, NY, USA. 30 August–3 September 2006; pp. 3521–3524.
    1. Gil E., Orini M., Bailón R., Vergara J.M., Mainardi L., Laguna P. Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. Physiol. Meas. 2010;31:1271. doi: 10.1088/0967-3334/31/9/015.
    1. Ma H.T., Zhang Y. Spectral analysis of pulse transit time variability and its coherence with other cardiovascular variabilities; Proceedings of the 28th 2006 EMBS’06 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; New York, NY, USA. 30 August–3 September 2006; pp. 6442–6445.
    1. Boulnois J.L. Photophysical processes in recent medical laser developments: A review. Lasers Med. Sci. 1986;1:47–66. doi: 10.1007/BF02030737.
    1. Martins R.M.S. Master’s Thesis. Universidade de Coimbra; Coimbra, Portugal: 2010. Fotopletismografia para Monitorização Cardí­aca para aplicação no Pulso.114p
    1. Gubbi S., Amrutur B. Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry. IEEE Trans. Biomed. Circuits Syst. 2015;9:272–283. doi: 10.1109/TBCAS.2014.2326712.
    1. Lee H., Chung H., Ko H., Lee J. Wearable Multichannel Photoplethysmography Framework for Heart Rate Monitoring During Intensive Exercise. IEEE Sens. J. 2018;18:2983–2993. doi: 10.1109/JSEN.2018.2801385.
    1. Haahr R., Duun S., Toft M., Belhage B., Larsen J., Birkelund K., Thomsen E. An Electronic Patch for Wearable Health Monitoring by Reflectance Pulse Oximetry. IEEE Trans. Biomed. Circuits Syst. 2012;6:45–53. doi: 10.1109/TBCAS.2011.2164247.
    1. Wang C.Z., Zheng Y.P. Home-Telecare of the elderly living alone using an new designed ear-wearable sensor; Proceedings of the 2008 5th International Summer School and Symposium on Medical Devices and Biosensors; Hong Kong, China. 1–3 June 2008; pp. 71–74.
    1. Rhee S., Yang B.H., Asada H. Artifact-resistant power-efficient design of finger-ring plethysmographic sensors. IEEE Trans. Biomed. Eng. 2001;48:795–805. doi: 10.1109/10.930904.
    1. Rosero G., Fernando O. Master’s Thesis. Engenharia de Sistemas Eletrônicos e de Automação, Universidade de Brasília; Brasília, Brasil: 2012. Sistema móvel de Monitoramento E Treinamento Para Ciclista com Smartphone Android.
    1. Allen J., Murray A. Similarity in bilateral photoplethysmographic peripheral pulse wave characteristics at the ears, thumbs and toes. Physiol. Meas. 2000;21:369. doi: 10.1088/0967-3334/21/3/303.
    1. Shelley K.H., Tamai D., Jablonka D., Gesquiere M., Stout R.G., Silverman D.G. The Effect of Venous Pulsation on the Forehead Pulse Oximeter Wave Form as a Possible Source of Error in SpO2 Calculation. Anesth. Analg. 2005;100:743–747. doi: 10.1213/01.ANE.0000145063.01043.4B.
    1. Mendelson Y., Duckworth R., Comtois G. A Wearable Reflectance Pulse Oximeter for Remote Physiological Monitoring; Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society; New York, NY, USA. 30 August–3 September 2006; pp. 912–915.
    1. Shin K., Kim Y., Bae S., Park K., Kim S. A Novel Headset with a Transmissive PPG Sensor for Heart Rate Measurement. In: Lim C.T., Goh J.C., editors. Proceedings of the 13th International Conference on Biomedical Engineering; Singapore. 3–6 December 2008; Berlin/Heidelberg, Germany: Springer; 2009. pp. 519–522.
    1. Paul B., Manuel M., Alex Z. Design and development of non invasive glucose measurement system; Proceedings of the 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1); Pune, India. 7–10 March 2012; pp. 43–46.
    1. Karlen W., Raman S., Ansermino J., Dumont G. Multiparameter Respiratory Rate Estimation from the Photoplethysmogram. IEEE Trans. Biomed. Eng. 2013;60:1946–1953. doi: 10.1109/TBME.2013.2246160.
    1. Araújo F.O. Infraestrutura De Hardware E Software Para Monitoramento De Batimentos Cardíacos Em Bovinos De Corte. Universidade Federal do Mato Grosso do Sul, FAENG, Campo Grande; Vila Olinda, Brasil: 2014. Trabalho de Conclusão de Curso em Engenharia Elétrica.
    1. Vanderlei L.C.M., Pastre C.M., Hoshi R.A., Carvalho T.D.D., Godoy M.F.D. Basic notions of heart rate variability and its clinical applicability. Braz. J. Cardiovasc. Surg. 2009;24:205–217. doi: 10.1590/S0102-76382009000200018.
    1. Javorka M., Zila I., Balhãrek T., Javorka K. Heart rate recovery after exercise: Relations to heart rate variability and complexity. Braz. J. Med. Biol. Res. 2002;35:991–1000. doi: 10.1590/S0100-879X2002000800018.
    1. Nunan D., Sandercock G.R., Brodie D.A. A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults. Pacing Clin. Electrophysiol. 2010;33:1407–1417. doi: 10.1111/j.1540-8159.2010.02841.x.
    1. Task F. Force of the European Society of Cardiology, Heart rate variability, standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93:1043–1065.
    1. Pumprla J., Howorka K., Groves D., Chester M., Nolan J. Functional assessment of heart rate variability: Physiological basis and practical applications. Int. J. Cardiol. 2002;84:1–14. doi: 10.1016/S0167-5273(02)00057-8.
    1. Hirfanoglu T., Serdaroglu A., Cetin I., Kurt G., Capraz I.Y., Ekici F., Arhan E., Bilir E. Effects of vagus nerve stimulation on heart rate variability in children with epilepsy. Epilepsy Behav. 2018;81:33–40. doi: 10.1016/j.yebeh.2018.01.036.
    1. Khaled A., Owis M., Mohamed S.A.A. Employing Time-Domain Methods and Poincaré Plot of Heart Rate Variability Signals to Detect Congestive Heart Failure. BIME J. 2006;6:35–41.
    1. De Carvalho T.D., Pastre C.M., Rossi R.C., de Abreu L., Valenti V., Vanderlei L.M. Índices geométricos de variabilidade da frequência cardíaca na doença pulmonar obstrutiva crônica. Rev. Port. Pneumol. 2011;17:260–265. doi: 10.1016/j.rppneu.2011.06.007.
    1. Smith A.L., Reynolds K.J., Owen H. Correlated Poincaré indices for measuring heart rate variability. Australas. Phys. Eng. Sci. Med. 2007;30:336.
    1. Lam J.C., Yan C.S., Lai A.Y., Tam S., Fong D.Y., Lam B., Ip M.S. Determinants of Daytime Blood Pressure in Relation to Obstructive Sleep Apnea in Men. Lung. 2009;187:291–298. doi: 10.1007/s00408-009-9161-7.
    1. Tulppo M.P., Mäkikallio T.H., Seppänen T., Laukkanen R.T., Huikuri H.V. Vagal modulation of heart rate during exercise: Effects of age and physical fitness. Am. J. Physiol. Heart Circ. Physiol. 1998;274:H424–H429. doi: 10.1152/ajpheart.1998.274.2.H424.
    1. Sarén-Koivuniemi T.J., Yli-Hankala A.M., van Gils M.J. Increased variation of the response index of nociception during noxious stimulation in patients during general anaesthesia. Comput. Methods Progr. Biomed. 2011;104:154–160. doi: 10.1016/j.cmpb.2010.10.001.
    1. Gamelin F.X., Berthoin S., Bosquet L. Validity of the polar S810 heart rate monitor to measure RR intervals at rest. Med. Sci. Sports Exerc. 2006;38:887–893. doi: 10.1249/01.mss.0000218135.79476.9c.
    1. Shields Robert W. Heart rate variability with deep breathing as a clinical test of cardiovagal function. Cleve. Clin. J. Med. 2009;76:S37–40. doi: 10.3949/ccjm.76.s2.08.
    1. Akselrod S., Gordon D., Ubel F., Shannon D., Berger A., Cohen R. Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science. 1981;213:220–222. doi: 10.1126/science.6166045.
    1. Elgendi M., Fletcher R.R., Norton I., Brearley M., Abbott D., Lovell N.H., Schuurmans D. Frequency analysis of photoplethysmogram and its derivatives. Comput. Methods Progr. Biomed. 2015;122:503–512. doi: 10.1016/j.cmpb.2015.09.021.
    1. Elgendi M. Standard Terminologies for Photoplethysmogram Signals. Curr. Cardiol. Rev. 2012;8:215–219. doi: 10.2174/157340312803217184.
    1. Higgins J.P. Nonlinear systems in medicine. Yale J. Biol. Med. 2002;75:247.
    1. Niskanen J.P., Tarvainen M.P., Ranta-aho P.O., Karjalainen P.A. Software for advanced HRV analysis. Comput. Methods Progr. Biomed. 2004;76:73–81. doi: 10.1016/j.cmpb.2004.03.004.
    1. Puri C., Ukil A., Bandyopadhyay S., Singh R., Pal A., Mandana K. iCarMa: Inexpensive Cardiac Arrhythmia Management–An IoT Healthcare Analytics Solution; Proceedings of the First IoT of Health ’16 Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems; Singapore. 25–30 June 2016; New York, NY, USA: ACM; 2016. pp. 3–8.
    1. Karegar F.P., Fallah A., Rashidi S. ECG based human authentication with using Generalized Hurst Exponent; Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE); Tehran, Iran. 2–4 May 2017; pp. 34–38.
    1. Pham T.D., Oyama-Higa M. Photoplethysmography technology and its feature visualization for cognitive stimulation assessment; Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT); Seville, Spain. 17–19 March 2015; pp. 1735–1740.
    1. Horio K., Li Y. Visualization and Analysis of Mental States Based on Photoplethysmogram; Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC); Kaohsiung, Taiwan. 7–9 December 2009; pp. 1401–1404.
    1. Lee J., Jung W., Kang I., Kim Y., Lee G. Design of filter to reject motion artifact of pulse oximetry. Comput. Stand. Interfaces. 2004;26:241–249. doi: 10.1016/S0920-5489(03)00077-1.
    1. Lee H.W., Lee J.W., Jung W.G., Lee G.K. The periodic moving average filter for removing motion artifacts from PPG signals. Int. J. Control Autom. Syst. 2007;5:701–706.
    1. Ruiz L.M., Manzo A., Casimiro E., Cárdenas E., González R. Heart rate variability using photoplethysmography with green wavelength; Proceedings of the 2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC); Ixtapa, Mexico. 5–7 Noember 2014; pp. 1–5.
    1. Manonelles Rincón L. Bachelor’s Thesis. Universitat Politècnica de Catalunya; Barcelona, Spain: 2017. Development of an Acquisition Circuit of Multiple Biological Signals for Integration into a Wearable Bracelet.
    1. Kukkapalli R. Bachelor’s Thesis. University of Maryland; Baltimore County, Baltimore, MD, USA: 2016. Non-Invasive Wearable Sensors for Respiration Monitoring.
    1. Asada H., Reisner A., Shaltis P., McCombie D. Towards the Development of Wearable Blood Pressure Sensors: A Photo-Plethysmograph Approach Using Conducting Polymer Actuators; Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference; Shanghai, China. 17–18 January 2005; pp. 4156–4159.
    1. Baheti P.K., Garudadri H. An Ultra Low Power Pulse Oximeter Sensor Based on Compressed Sensing; Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks; Berkeley, CA, USA. 3–5 June 2009; pp. 144–148.
    1. Wang L., Lo B.P., Yang G.Z. Multichannel Reflective PPG Earpiece Sensor With Passive Motion Cancellation. IEEE Trans. Biomed. Circuits Syst. 2007;1:235–241. doi: 10.1109/TBCAS.2007.910900.
    1. Rhee S., Liu S. An ultra-low power, self-organizing wireless network and non-invasive biomedical instrumentation; Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Engineering in Medicine and Biology; Houston, TX, USA. 23–26 October 2002; pp. 1803–1804.
    1. Guyton A., Hall J. Renal regulation of potassium, calcium, phosphate, and magnesium; integration of renal mechanisms for control of blood volume and extracellular fluid volume. Guyton Hall Textb. Med. Physiol. 2006;10:371–373.
    1. Silva M.D.C. Ph.D. Thesis. Programa de Pós-Graduação em Informática, Universidade Federal da Paraíba, UFPB; João Pessoa, Brasil: 2012. Monitoramento Remoto Preventivo de Pacientes Com Doenças Cardiovasculares Utilizando Dispositivo Móvel Como Agente Inteligente.
    1. Pantoni C., Reis M., Martins L., Catai A., Costa D., Borghi-Silva A. Study of heart rate autonomic modulation at rest in elderly patients with chronic obstructive pulmonary disease. Braz. J. Phys. Ther. 2007;11:35–41.
    1. Pickett J., Amoroso P., Nield D., Jones D. Pulse oximetry and PPG measurements in plastic surgery. Engineering in Medicine and Biology Society, 1997. ; Proceedings of the 19th Annual International Conference of the IEEE; Chicago, IL, USA. 30 October–2 November 1997; pp. 2330–2332.
    1. Allen J., Oates C.P., Lees T.A., Murray A. Photoplethysmography detection of lower limb peripheral arterial occlusive disease: A comparison of pulse timing, amplitude and shape characteristics. Physiol. Meas. 2005;26:811. doi: 10.1088/0967-3334/26/5/018.
    1. Custódio Rubira M., Angelis Rubira A.P.F.D., Silva Soares P.P.D., Gusmão Medeiros L., Alves Neves G., Consolim-Colombo F.M. Cardiovascular risk in eutrophic young subjects: Influence of corporal fat and sympathetic activity. ConSci. Saúde. 2011;10:223–230.
    1. Amir O., Barak-Shinar D., Henry A., Smart F.W. Photoplethysmography as a single source for analysis of sleep-disordered breathing in patients with severe cardiovascular disease. J. Sleep Res. 2012;21:94–100. doi: 10.1111/j.1365-2869.2011.00927.x.
    1. Melillo P., Fusco R., Sansone M., Bracale M., Pecchia L. Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Med. Biol. Eng. Comput. 2011;49:67–74. doi: 10.1007/s11517-010-0728-5.
    1. Carnethon M.R., Liao D., Evans G.W., Cascio W.E., Chambless L.E., Heiss G. Correlates of the shift in heart rate variability with an active postural change in a healthy population sample: The Atherosclerosis Risk In Communities study. Am. Heart J. 2002;143:808–813. doi: 10.1067/mhj.2002.121928.
    1. Naydenova E., Tsanas A., Casals-Pascual C., Vos M.D. Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings; Proceedings of the 2015 IEEE Global Humanitarian Technology Conference (GHTC); Seattle, WA, USA. 8–11 October 2015; pp. 377–384.
    1. Karlen W., Brouse C., Cooke E., Ansermino J., Dumont G. Respiratory rate estimation using respiratory sinus arrhythmia from photoplethysmography; Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Boston, MA, USA. 30 August–3 September 2011; pp. 1201–1204.
    1. Furuland H., Linde T., Englund A., Wikström B. Heart rate variability is decreased in chronic kidney disease but may improve with hemoglobin normalization. J. Nephrol. 2008;21:45–52.
    1. Sommermeyer D., Zou D., Ficker J.H., Randerath W., Fischer C., Penzel T., Sanner B., Hedner J., Grote L. Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm. Med. Biol. Eng. Comput. 2016;54:1111–1121. doi: 10.1007/s11517-015-1410-8.
    1. Sánchez D.M. Diseño De Un Dispositivo Para La Detección Del Estrés A Partir De La Señal De Fotopletismografía. [(accessed on 08 June 2018)];2014 Trabajo Fin de G, Escuela Técnica Superior De Ingeniería Grado En Ingeniería De Las Tecnologías De Telecomunicación, Sevilla. Available online:
    1. Vilhegas L.Z. Master’s Thesis. Título de Engenharia Elétrica, Escola Politécnica; São Paulo, Brasil: 2007. Development of a Prototype for Monitoring Oxygen Saturation and Heart Rate for Rodents.
    1. Hickey M., Samuels N., Randive N., Langford R., Kyriacou P. A new fibre optic pulse oximeter probe for monitoring splanchnic organ arterial blood oxygen saturation. Comput. Methods Progr. Biomed. 2012;108:883–888. doi: 10.1016/j.cmpb.2011.03.019.
    1. Li D., Zhao H., Dou S. A new signal decomposition to estimate breathing rate and heart rate from photoplethysmography signal. Biomed. Signal Process. Control. 2015;19:89–95. doi: 10.1016/j.bspc.2015.03.008.
    1. Jovanov E., Nallathimmareddygari V., Pryor J. SmartStuff: A case study of a smart water bottle; Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Orlando, FL, USA. 16–20 August 2016; pp. 6307–6310.
    1. Da Cruz M.A.A., Rodrigues J.J.P.C., Al-Muhtadi J., Korotaev V.V., de Albuquerque V.H.C. A Reference Model for Internet of Things Middleware. IEEE Internet Things J. 2018;5:871–883. doi: 10.1109/JIOT.2018.2796561.
    1. Rodrigues J.J.P.C., Segundo D.B.D.R., Junqueira H.A., Sabino M.H., Prince R.M., Al-Muhtadi J., Albuquerque V.H.C.D. Enabling Technologies for the Internet of Health Things. IEEE Access. 2018;6:13129–13141. doi: 10.1109/ACCESS.2017.2789329.
    1. Lakshmanaprabu S.K., Shankar K., Khanna A., Gupta D., Rodrigues J.J.P.C., Pinheiro P.R., Albuquerque V.H.C.D. Effective Features to Classify Big Data Using Social Internet of Things. IEEE Access. 2018;6:24196–24204. doi: 10.1109/ACCESS.2018.2830651.
    1. Rodrigues J., Segundo D., Junqueira H., Sabino M., Prince R., Al-Muhtadi J., de Albuquerque V. Enabling Technologies for the Internet of Health Things. IEEE Access. 2018;6:13129–13141. doi: 10.1109/ACCESS.2017.2789329.
    1. Woo M.W., Lee J., Park K. A reliable IoT system for Personal Healthcare Devices. Future Gener. Comput. Syst. 2018;78:626–640. doi: 10.1016/j.future.2017.04.004.
    1. Farahani B., Firouzi F., Chang V., Badaroglu M., Constant N., Mankodiya K. Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Gener. Comput. Syst. 2018;78:659–676. doi: 10.1016/j.future.2017.04.036.
    1. Islam S., Kwak D., Kabir M., Hossain M., Kwak K. The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access. 2015;3:678–708. doi: 10.1109/ACCESS.2015.2437951.
    1. Constant N., Douglas-Prawl O., Johnson S., Mankodiya K. Pulse-Glasses: An unobtrusive, wearable HR monitor with Internet-of-Things functionality; Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN); Cambridge, MA, USA. 9–12 June 2015; pp. 1–5.
    1. Wannenburg J., Malekian R. Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System. IEEE Sens. J. 2015;15:6839–6852. doi: 10.1109/JSEN.2015.2464773.
    1. Bobbia S., Macwan R., Benezeth Y., Mansouri A., Dubois J. Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognit. Lett. 2017 doi: 10.1016/j.patrec.2017.10.017.
    1. Wijshoff R., Mischi M., Aarts R. Reduction of Periodic Motion Artifacts in Photoplethysmography. IEEE Trans. Biomed. Eng. 2017;64:196–207. doi: 10.1109/TBME.2016.2553060.
    1. Yuan H., Memon S.F., Newe T., Lewis E., Leen G. Motion artefact minimization from photoplethysmography based non-invasive hemoglobin sensor based on an envelope filtering algorithm. Measurement. 2018;115:288–298. doi: 10.1016/j.measurement.2017.10.060.
    1. Ram M.R., Madhav K.V., Krishna E.H., Komalla N.R., Reddy K.A. A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter. IEEE Trans. Instrum. Meas. 2012;61:1445–1457. doi: 10.1109/TIM.2011.2175832.
    1. Relente A., Sison L. Characterization and adaptive filtering of motion artifacts in pulse oximetry using accelerometers; Proceedings of the Second Joint IEEE 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, Engineering in Medicine and Biology; Houston, TX, USA. 23–26 October 2002; pp. 1769–1770.
    1. Chan K., Zhang Y. Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter; Proceedings of the IEEE Sensors; Orlando, FL, USA. 12–14 June 2002; pp. 1343–1346.
    1. Lee C., Zhang Y.T. Reduction of motion artifacts from photoplethysmographic recordings using a wavelet denoising approach; Proceedings of the 2003 IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, IEEE; Kyoto, Japan. 20–22 October 2003; pp. 194–195.
    1. Raghuram M., Madhav K.V., Krishna E.H., Reddy K.A. Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals; Proceedings of the 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010); Kuala Lumpur, Malaysia. 10–13 May 2010; pp. 460–463.
    1. Alfaouri M., Daqrouq K. ECG signal denoising by wavelet transform thresholding. Am. J. Appl. Sci. 2008;5:276–281. doi: 10.3844/ajassp.2008.276.281.
    1. Coetzee F.M., Elghazzawi Z. Noise-resistant pulse oximetry using a synthetic reference signal. IEEE Trans. Biomed. Eng. 2000;47:1018–1026. doi: 10.1109/10.855928.
    1. Cvetkovic D., Übeyli E.D., Cosic I. Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study. Digit. Signal Process. 2008;18:861–874. doi: 10.1016/j.dsp.2007.05.009.
    1. Raghuram M., Madhav K.V., Krishna E.H., Komalla N.R., Sivani K., Reddy K.A. Dual-tree complex wavelet transform for motion artifact reduction of PPG signals; Proceedings of the 2012 IEEE International Symposium on Medical Measurements and Applications Proceedings; Budapest, Hungary. 18–19 May 2012; pp. 1–4.

Source: PubMed

3
S'abonner