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Journal articles on the topic 'Photoplethysmography'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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1

Dorsey, J. Stonewall. "PHOTOPLETHYSMOGRAPHY." Plastic and Reconstructive Surgery 76, no. 5 (November 1985): 800. http://dx.doi.org/10.1097/00006534-198511000-00038.

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2

Alian, Aymen A., and Kirk H. Shelley. "Photoplethysmography." Best Practice & Research Clinical Anaesthesiology 28, no. 4 (December 2014): 395–406. http://dx.doi.org/10.1016/j.bpa.2014.08.006.

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3

Lindberg, L. G., T. Tamura, and P. Å. Öberg. "Photoplethysmography." Medical & Biological Engineering & Computing 29, no. 1 (January 1991): 40–47. http://dx.doi.org/10.1007/bf02446294.

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4

Lindberg, L. G., and P. Å. Öberg. "Photoplethysmography." Medical & Biological Engineering & Computing 29, no. 1 (January 1991): 48–54. http://dx.doi.org/10.1007/bf02446295.

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5

Peng, Rong-Chao, Wen-Rong Yan, Ning-Ling Zhang, Wan-Hua Lin, Xiao-Lin Zhou, and Yuan-Ting Zhang. "Investigation of Five Algorithms for Selection of the Optimal Region of Interest in Smartphone Photoplethysmography." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6830152.

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Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI), which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR), spectral energy ratio (SER), template matching (TM), temporal difference (TD), and gradient (GRAD). Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm) and smaller limits of agreement (<3 bpm). The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical.
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6

KORHONEN, I., and A. YLI-HANKALA. "Photoplethysmography and nociception." Acta Anaesthesiologica Scandinavica 53, no. 8 (September 2009): 975–85. http://dx.doi.org/10.1111/j.1399-6576.2009.02026.x.

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7

Gailite, L., J. Spigulis, and A. Lihachev. "Multilaser photoplethysmography technique." Lasers in Medical Science 23, no. 2 (July 14, 2007): 189–93. http://dx.doi.org/10.1007/s10103-007-0471-9.

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8

Ju, Bin, Yun Tao Qian, and Huo Jie Ye. "Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging." Applied Mechanics and Materials 380-384 (August 2013): 773–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.773.

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[Purpose] Smartphones video cameras can be used to detect the photoplethysmograph (PPG) signal.The pulse wave signal detected by smartphone always mixed mass noise because of finger moving, unevenness of pressure and outer light interference. Previous studies limit to the filtering algorithm that denoising signals, without considering characteristics information of pulse wave itself. [Method] In this paper, we propose an algorithm based on wavelet to detect qualified PPG, which captures three critical characteristic quantities through wavelet high frequency coefficient. [Results] Experiment illustrates that the detected PPG signal contain dicrotic wave, and whats more, further experiment on artery elasticity indexes indicates good robust of the algorithm. [Conclusions] Wavelet Based Measurement on Photoplethysmography by Smartphone Imaging can be used for the calculation of cardiovascular parameter such as angiosclerosis, arrhythmia, and vascular resistance.
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9

Yen, Chih-Ta, Sheng-Nan Chang, and Cheng-Hong Liao. "Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions." Measurement and Control 54, no. 3-4 (March 2021): 439–45. http://dx.doi.org/10.1177/00202940211001904.

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This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.
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10

Hayes, Matthew J., and Peter R. Smith. "Artifact reduction in photoplethysmography." Applied Optics 37, no. 31 (November 1, 1998): 7437. http://dx.doi.org/10.1364/ao.37.007437.

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11

Komatsu, Kan-ichiro, Toshio Fukutake, and Takamichi Hattori. "Fingertip photoplethysmography and migraine." Journal of the Neurological Sciences 216, no. 1 (December 2003): 17–21. http://dx.doi.org/10.1016/s0022-510x(03)00208-9.

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12

Nilsson, Lena M. "Respiration Signals from Photoplethysmography." Anesthesia & Analgesia 117, no. 4 (October 2013): 859–65. http://dx.doi.org/10.1213/ane.0b013e31828098b2.

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13

BOURDILLON, NICOLAS, MASIH NILCHIAN, and GRÉGOIRE P. MILLET. "Photoplethysmography Detection of Overreaching." Medicine & Science in Sports & Exercise 51, no. 4 (April 2019): 701–7. http://dx.doi.org/10.1249/mss.0000000000001836.

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14

Kamal, A. A. R., J. B. Harness, G. Irving, and A. J. Mearns. "Skin photoplethysmography — a review." Computer Methods and Programs in Biomedicine 28, no. 4 (April 1989): 257–69. http://dx.doi.org/10.1016/0169-2607(89)90159-4.

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15

Burke, M. J., and M. V. Whelan. "Photoplethysmography: Selecting optoelectronic components." Medical & Biological Engineering & Computing 24, no. 6 (November 1986): 647–50. http://dx.doi.org/10.1007/bf02446270.

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16

Sahni, Rakesh. "Noninvasive Monitoring by Photoplethysmography." Clinics in Perinatology 39, no. 3 (September 2012): 573–83. http://dx.doi.org/10.1016/j.clp.2012.06.012.

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17

Feukeu, Etienne Alain, and Simon Winberg. "Photoplethysmography Heart Rate Monitoring." International Journal of E-Health and Medical Communications 12, no. 3 (May 2021): 17–37. http://dx.doi.org/10.4018/ijehmc.20210501.oa2.

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Research conducted by the World Health Organisation (WHO) in 2018 demonstrated that the worldwide threat of cardiovascular diseases (CVDs) has increased compared to previous years. CVDs are very dangerous: if timely treatment is not performed, these conditions could become irreversible and lead to sudden death. Prescriptive measures include physical exercises and monitoring of the heart rate (HR). Despite the existence of various HR monitoring devices (or HMDs), a major challenge remains their availability, particularly to people in lower-income countries. Unfortunately, it is also this segment of the population that is the most vulnerable to CVDs. Accordingly, this led the authors to propose the design for an easily constructible state-of-the-art HMD that attempts to provide a highly accessible, lower-cost, and long-lasting solution that would be more affordable and accessible to these low-income communities.
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18

Antonsen, Lars Prag, and Knut Arvid Kirkebøen. "Evaluation of Fluid Responsiveness: Is Photoplethysmography a Noninvasive Alternative?" Anesthesiology Research and Practice 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/617380.

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Background. Goal-directed fluid therapy reduces morbidity and mortality in various clinical settings. Respiratory variations in photoplethysmography are proposed as a noninvasive alternative to predict fluid responsiveness during mechanical ventilation. This paper aims to critically evaluate current data on the ability of photoplethysmography to predict fluid responsiveness.Method. Primary searches were performed in PubMed, Medline, and Embase on November 10, 2011.Results. 14 papers evaluating photoplethysmography and fluid responsiveness were found. Nine studies calculated areas under the receiver operating characteristic curves forΔPOP (>0.85 in four, 0.75–0.85 in one, and <0.75 in four studies) and seven for PVI (values ranging from 0.54 to 0.98). Correlations betweenΔPOP/PVI andΔPP/other dynamic variables vary substantially.Conclusion. Although photoplethysmography is a promising technique, predictive values and correlations with other hemodynamic variables indicating fluid responsiveness vary substantially. Presently, it is not documented that photoplethysmography is adequately valid and reliable to be included in clinical practice for evaluation of fluid responsiveness.
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19

Jeong, Jae Hoon, Sung Min Kim, Sung Yun Park, and Sangjoon Lee. "A Study on Measurement of Photoplethysmograph Using a Smartphone Camera." Applied Mechanics and Materials 479-480 (December 2013): 137–42. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.137.

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In this study, we proposed a method for measuring photoplethysmographic using a smartphone camera. A development algorithm is consists 6 procedures. The first is to convert RGB to Gray level from a camera image, the second is to detect ROI from image, the third is to extract photoplethysmography signal from a camera image, the fourth is to filter baseline, and the last is to oversample procedure using cubic spline interpolation. The proposed algorithm has been tested using several smartphone with a person and which can effectively acquire persons PPG signal at any situation. We supposed that the proposed algorithm can easily adapt for a smartphone m-health system.
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20

Sharif-Kashani, Babak, Neda Behzadnia, Payman Shahabi, and Makan Sadr. "Screening for Deep Vein Thrombosis in Asymptomatic High-risk Patients: A Comparison between Digital Photoplethysmography and Venous Ultrasonography." Angiology 60, no. 3 (October 14, 2008): 301–7. http://dx.doi.org/10.1177/0003319708323494.

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Objective To determine the role of digital photoplethysmography in screening asymptomatic patients who are susceptible for developing deep vein thrombosis. Methods Three hundred and thirty-seven limbs in 169 patients who were high risk for development of deep vein thrombosis were assessed by ultrasonography digital photoplethysmography and the results were compared. Results Thirteen limbs were found to have deep vein thrombosis as demonstrated by ultrasonography. All limbs with a venous refilling time greater than 12 seconds had a normal ultrasonography. Compared with ultrasonography and using refilling time less than 12 seconds as the cutoff point, digital photoplethysmography achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 100%, 73.8%, 13.3%, and 100% respectively, for detecting deep vein thrombosis in asymptomatic high-risk patients. Conclusion Digital photoplethysmography is a simple, noninvasive, and highly sensitive test for screening of deep vein thrombosis.
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21

Kozioł, Maciej, Piotr Piech, Marcin Maciejewski, and Wojciech Surtel. "The latest applications of photoplethysmography." Acta Angiologica 25, no. 1 (March 20, 2019): 28–34. http://dx.doi.org/10.5603/aa.2019.0005.

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22

Maeda, Yuka, Masaki Sekine, Toshiyo Tamura, Takuji Suzuki, and Ken-ichi Kameyama. "Performance evaluation of green photoplethysmography." Journal of Life Support Engineering 19, Supplement (2007): 183. http://dx.doi.org/10.5136/lifesupport.19.supplement_183.

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23

Deshpande, Alaka, Sadhana A. Mandlik, Aparna S. Lakhe, Jyoti V. Jethe, and Vinnet Sinha. "Photoplethysmography and Its Clinical Application." MGM Journal of Medical Sciences 4, no. 2 (2017): 89–96. http://dx.doi.org/10.5005/jp-journals-10036-1146.

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24

Turcott, Robert G., and Todd J. Pavek. "Hemodynamic sensing using subcutaneous photoplethysmography." American Journal of Physiology-Heart and Circulatory Physiology 295, no. 6 (December 2008): H2560—H2572. http://dx.doi.org/10.1152/ajpheart.00574.2008.

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Pacemakers and implantable defibrillators presently operate without access to hemodynamic information. If available, such data would allow tailoring of delivered therapy according to perfusion status, optimization of device function, and enhancement of disease monitoring and management. A candidate method for hemodynamic sensing in these devices is photoplethysmography (PPG), which uses light to noninvasively detect changes in blood volume. The present study tested the hypotheses that PPG can function in a subcutaneous location, that the acute changes in blood volume it detects are directly proportional to changes in arterial pressure, and that optimum pacing intervals identified by it are concordant with those determined by arterial pressure. Aortic pressure and PPG were simultaneously recorded in 10 dogs under general anesthesia during changes in atrioventricular (AV) delay and bursts of rapid pacing to simulate tachyarrhythmias. Direct proportionality between transient changes in pressure and PPG waveforms was tested using regression analysis. Scatter plots had a linear appearance, with correlation coefficients of 0.95 (SD 0.03) and 0.72 (SD 0.24) for rapid-pacing and AV delay protocols, respectively. The data were well described by a directly proportional relationship. Optimum AV delays estimated from the induced changes in aortic pressure and PPG waveforms were concordant. This preliminary canine study demonstrates that PPG can function subcutaneously and that it may serve as a surrogate for acute changes in arterial pressure.
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Allen, John, Klaus Overbeck, Gerard Stansby, and Alan Murray. "Photoplethysmography Assessments in Cardiovascular Disease." Measurement and Control 39, no. 3 (April 2006): 80–83. http://dx.doi.org/10.1177/002029400603900303.

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26

Akl, Tony J., Mark A. Wilson, M. Nance Ericson, and Gerard L. Coté. "Intestinal perfusion monitoring using photoplethysmography." Journal of Biomedical Optics 18, no. 8 (August 12, 2013): 087005. http://dx.doi.org/10.1117/1.jbo.18.8.087005.

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27

Holton, Benjamin D., Kavan Mannapperuma, Peter J. Lesniewski, and John C. Thomas. "Signal recovery in imaging photoplethysmography." Physiological Measurement 34, no. 11 (October 22, 2013): 1499–511. http://dx.doi.org/10.1088/0967-3334/34/11/1499.

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28

Turcott, Robert G., and Todd J. Pavek. "Pacing interval optimization using photoplethysmography." Journal of Cardiac Failure 10, no. 4 (August 2004): S73. http://dx.doi.org/10.1016/j.cardfail.2004.06.203.

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29

O’Neill, Christopher. "Haptic media and the cultural techniques of touch: The sphygmograph, photoplethysmography and the Apple Watch." New Media & Society 19, no. 10 (July 18, 2017): 1615–31. http://dx.doi.org/10.1177/1461444817717514.

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This article draws upon cultural techniques theory to propose an approach to studying haptic media as media technologies which train or discipline touch and which serve to produce touch itself as a coherent and ‘proper’ communicative technology. This article analyses the different forms of touch which have coalesced around the sphygmograph, a nineteenth-century pulse writing technology, and photoplethysmography, a contemporary heart rate–measuring technology which has been remediated as part of the Apple Watch. This article demonstrates that nineteenth-century clinicians drew upon the sphygmograph to authorise doctorly touch as newly ‘proper’ within a changed technological context. By contrast, an analysis of the place of error within the Apple Watch’s photoplethysmograph demonstrates how contemporary self-quantifiers are encumbered with an unreliable measuring apparatus which can only generalise a form of ‘improper’ touch, touch which fails to know the body and which remains tied to a ‘proper’ touch which lies elsewhere.
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30

Snizhko, Y. M., O. O. Boiko, N. P. Botsva, D. V. Chernetchenko, and M. M. Milyh. "Methods for increasing the accuracy of recording the parameters of the cardiovascular system in double-beam photoplethysmography." Regulatory Mechanisms in Biosystems 9, no. 3 (July 24, 2018): 335–39. http://dx.doi.org/10.15421/021849.

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Photoplethysmography has recently become more widespread among non-invasive methods for obtaining information on the state of physiological systems of the human body. Serial photoplethysmographs are intended for use in clinics and require special care, therefore, interest in portable media developed on the basis of modern sensors and microcontrollers is growing, which would not only make this method available for individual use, but also expand its capabilities through the use of light of various spectral ranges. Such devices require modified signal processing techniques that allow them to be used in mobile applications. The aim of the work is to develop methods for processing signals from a modern two-beam sensor operating in the red and infrared ranges for the analysis of photoplethysmography on a mobile device (smartphone or tablet). A device using the microcontroller and radio module in the Bluetooth standard allows you to continuously record pulse waves, determine the level of oxygen in the blood, calculate peak-peak intervals and heart rate. The use of the two-beam sensor for registration and the implementation of the developed signal processing methods in the Android operation system application increase the accuracy of setting the maximums on pulse curve and provide a relative error in determining the heart rate and pulse-to-pulse intervals relative to the certified electrocardiograph at 9.2% and 9.6% respectively, with an average level of interference and an average activity. An Android operation system mobile device (tablet, smartphone) allows you to visualize the measurement results, store data in the internal memory, and transfer them to the server for further processing.
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Zangróniz, Roberto, Arturo Martínez-Rodrigo, María López, José Pastor, and Antonio Fernández-Caballero. "Estimation of Mental Distress from Photoplethysmography." Applied Sciences 8, no. 1 (January 5, 2018): 69. http://dx.doi.org/10.3390/app8010069.

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32

Solem, Kristian, Bo Olde, and Leif Sörnmo. "Prediction of Intradialytic Hypotension Using Photoplethysmography." IEEE Transactions on Biomedical Engineering 57, no. 7 (July 2010): 1611–19. http://dx.doi.org/10.1109/tbme.2010.2042170.

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Kyriacou, P. A., S. Powell, R. M. Langford, and D. P. Jones. "Esophageal pulse oximetry utilizing reflectance photoplethysmography." IEEE Transactions on Biomedical Engineering 49, no. 11 (November 2002): 1360–68. http://dx.doi.org/10.1109/tbme.2002.804584.

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34

Jespersen, Lennard Tang, and Ole Lederballe Pedersen. "The quantitative aspect of photoplethysmography revised." Heart and Vessels 2, no. 3 (September 1986): 186–90. http://dx.doi.org/10.1007/bf02128146.

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Yoon, Young-Zoon, and Gil-Won Yoon. "Nonconstrained Blood Pressure Measurement by Photoplethysmography." Journal of the Optical Society of Korea 10, no. 2 (June 1, 2006): 91–95. http://dx.doi.org/10.3807/josk.2006.10.2.091.

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36

Trumpp, Alexander, Joachim Schell, Hagen Malberg, and Sebastian Zaunseder. "Vasomotor assessment by camera-based photoplethysmography." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 199–202. http://dx.doi.org/10.1515/cdbme-2016-0045.

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AbstractCamera-based photoplethysmography (cbPPG) is a novel technique that allows the contactless acquisition of cardio-respiratory signals. Previous works on cbPPG most often focused on heart rate extraction. This contribution is directed at the assessment of vasomotor activity by means of cameras. In an experimental study, we show that vasodilation and vasoconstriction both lead to significant changes in cbPPG signals. Our findings underline the potential of cbPPG to monitor vasomotor functions in real-life applications.
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37

VERAART, JOEP C. J. M., A. M. JOOST VAN DER KLEY, and H. A. MARTINO NEUMANN. "Digital Photoplethysmography and Light Reflection Rheography." Journal of Dermatologic Surgery and Oncology 20, no. 7 (July 1992): 470–73. http://dx.doi.org/10.1111/j.1524-4725.1992.tb03219.x.

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VERAART, JOEP C. J. M., A. M. JOOST VAN DER KLEY, and H. A. MARTINO NEUMANN. "Digital Photoplethysmography and Light Reflection Rheography." Journal of Dermatologic Surgery and Oncology 20, no. 7 (July 1994): 470–73. http://dx.doi.org/10.1111/j.1524-4725.1994.tb03219.x.

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39

Akl, Tony J., Mark A. Wilson, M. Nance Ericson, and Gerard L. Coté. "Quantifying tissue mechanical properties using photoplethysmography." Biomedical Optics Express 5, no. 7 (June 19, 2014): 2362. http://dx.doi.org/10.1364/boe.5.002362.

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Stojanovic, R., and D. Karadaglic. "A LED–LED-based photoplethysmography sensor." Physiological Measurement 28, no. 6 (May 3, 2007): N19—N27. http://dx.doi.org/10.1088/0967-3334/28/6/n01.

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Fleischhauer, Vincent, Nora Ruprecht, Michele Sorelli, Leonardo Bocchi, and Sebastian Zaunseder. "Pulse decomposition analysis in photoplethysmography imaging." Physiological Measurement 41, no. 9 (October 6, 2020): 095009. http://dx.doi.org/10.1088/1361-6579/abb005.

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van Gastel, Mark, Sander Stuijk, and Gerard de Haan. "Robust respiration detection from remote photoplethysmography." Biomedical Optics Express 7, no. 12 (November 3, 2016): 4941. http://dx.doi.org/10.1364/boe.7.004941.

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Laurie, Jordan, Niall Higgins, Thierry Peynot, and Jonathan Roberts. "Dedicated Exposure Control for Remote Photoplethysmography." IEEE Access 8 (2020): 116642–52. http://dx.doi.org/10.1109/access.2020.3003548.

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Eerikäinen, Linda M., Alberto G. Bonomi, Lukas R. C. Dekker, and Ronald M. Aarts. "Atrial Fibrillation Episodes Detected Using Photoplethysmography." Journal of the American College of Cardiology 75, no. 11 (March 2020): 1365. http://dx.doi.org/10.1016/j.jacc.2019.10.064.

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Zabolotskikh, B. I., and S. I. Yuran. "Portable photoplethysmography system for biomedical research." Measurement Techniques 42, no. 4 (April 1999): 353–56. http://dx.doi.org/10.1007/bf02504396.

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Knorr-Chung, Bethany R., Susan P. McGrath, and George T. Blike. "Identifying Airway Obstructions Using Photoplethysmography (PPG)." Journal of Clinical Monitoring and Computing 22, no. 2 (January 25, 2008): 95–101. http://dx.doi.org/10.1007/s10877-008-9110-7.

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47

López-Silva, S. M., M. L. Dotor, and R. Giannetti. "On laparoscopic photoplethysmography and pulse oximetry." Journal of Clinical Monitoring and Computing 24, no. 3 (June 2010): 219–20. http://dx.doi.org/10.1007/s10877-010-9239-z.

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48

Tomita, Keisuke, Taka-aki Nakada, Taku Oshima, Takehiko Oami, Tuerxun Aizimu, and Shigeto Oda. "Non-invasive monitoring using photoplethysmography technology." Journal of Clinical Monitoring and Computing 33, no. 4 (October 4, 2018): 637–45. http://dx.doi.org/10.1007/s10877-018-0205-5.

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49

Vulcan, Ramona S., Stephanie André, and Marie Bruyneel. "Photoplethysmography in Normal and Pathological Sleep." Sensors 21, no. 9 (April 22, 2021): 2928. http://dx.doi.org/10.3390/s21092928.

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This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.
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Killian, Jacquelin M., Rachel M. Radin, Cubby L. Gardner, Lalon Kasuske, Kylee Bashirelahi, Dominic Nathan, David O. Keyser, Christopher J. Cellucci, David Darmon, and Paul E. Rapp. "Alternative Devices for Heart Rate Variability Measures: A Comparative Test–Retest Reliability Study." Behavioral Sciences 11, no. 5 (May 2, 2021): 68. http://dx.doi.org/10.3390/bs11050068.

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Abstract:
Using healthy adult participants, seven measures of heart rate variability were obtained simultaneously from four devices in five behavioral conditions. Two devices were ECG-based and two utilized photoplethysmography. The 140 numerical values (measure, condition, device) are presented. The comparative operational reliability of the four devices was assessed, and it was found that the two ECG-base devices were more reliable than the photoplethysmographic devices. The interchangeability of devices was assessed by determining the between-device Limits of Agreement. Intraclass correlation coefficients were determined and used to calculate the standard error of measurement and the Minimal Detectable Difference. The Minimal Detectable Difference, MDD, quantifies the smallest statistically significant change in a measure and is therefore critical when HRV measures are used longitudinally to assess treatment response or disease progression.
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