Skip to main content
SpringerLink
Log in

A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG)

  • Review
  • Published:
Journal of Cardiovascular Translational Research Aims and scope Submit manuscript

Abstract

A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. García-López I, Rodriguez-Villegas E. Extracting the jugular venous pulse from anterior neck contact photoplethysmography. Sci Rep. 2020;10. https://doi.org/10.1038/s41598-020-60317-7.

  2. Lazim MRMLM, Aminuddin A, Chellappan K, Ugusman A, Hamid AA, Ahmad WANW, Mohamad MSF. Is heart rate a confounding factor for photoplethysmography markers? A systematic review, Int J Environ Res Public Health. 2020;17. https://doi.org/10.3390/ijerph17072591.

  3. Zaunseder S, Trumpp A, Wedekind D, Malberg H. Cardiovascular assessment by imaging photoplethysmography-a review. Biomedizinische Technik. 2018;63. https://doi.org/10.1515/bmt-2017-0119.

  4. Almarshad MA, Islam MS, Al-Ahmadi S, BaHammam AS. Diagnostic features and potential applications of PPG signal in healthcare: a systematic review. Healthcare. 2022;10:547. https://doi.org/10.3390/healthcare10030547.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Biswas D, Simoes-Capela N, Van Hoof C, Van Helleputte N. Heart rate estimation from wrist-worn photoplethysmography: a review. IEEE Sens J. 2019;19. https://doi.org/10.1109/JSEN.2019.2914166.

  6. Daimiwal N, Sundhararajan M, Shriram R. Respiratory rate, heart rate and continuous measurement of BP using PPG, in: International Conference on Communication and Signal Processing. ICCSP 2014 – Proceedings. 2014. https://doi.org/10.1109/ICCSP.2014.6949996.

  7. Tjahjadi H, Ramli K. Noninvasive blood pressure classification based on photoplethysmography using K-nearest neighbors’ algorithm: a feasibility study. Information (Switzerland). 2020;11. https://doi.org/10.3390/info11020093.

  8. Namvari M, Lipoth J, Knight S, Jamali AA, Hedayati M, Spiteri RJ, Syed-Abdul S. Photoplethysmography enabled wearable devices and stress detection: a scoping review. J Pers Med. 2022;12. https://doi.org/10.3390/jpm12111792.

  9. Djeldjli D, Bousefsaf F, Maaoui C, Bereksi-Reguig F, Pruski A. Remote estimation of pulse wave features related to arterial stiffness and blood pressure using a camera. Biomed Signal Process Control. 2021;64. https://doi.org/10.1016/j.bspc.2020.102242.

  10. Sun X, Liu C, Zhang Y, Yang L. An automatic instrument for brachial and ankle systolic pressure measurement using photoplethysmography. In: 3rd International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2009. 2009. https://doi.org/10.1109/ICBBE.2009.5162312.

  11. Y. Aarthi, B. Karthikeyan, N.P. Raj, M. Ganesan, Fingertip based estimation of heart rate using photoplethysmography, in: 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 2019. https://doi.org/10.1109/ICACCS.2019.8728432.

  12. Meddah K, Kedir-Talha M, Zairi H. FPGA-based system for heart rate calculation based on PPG signal. In: 2017 5th International Conference on Electrical Engineering - Boumerdes, ICEE-B 2017. 2017. https://doi.org/10.1109/ICEE-B.2017.8192157.

  13. Johnson MA, Jegan R, Anitha Mary X. Performance measures on blood pressure and heart rate measurement from PPG signal for biomedical applications. In: Proceedings of IEEE International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology, ICIEEIMT 2017, 2017. https://doi.org/10.1109/ICIEEIMT.2017.8116856.

  14. Das S, Pal S, Mitra M. Real time heart rate detection from PPG signal in noisy environment. In: 2016 International Conference on Intelligent Control, Power and Instrumentation, ICICPI 2016, 2017. https://doi.org/10.1109/ICICPI.2016.7859676.

  15. Yang D, Zhu J, Zhu P. SpO2 and heart rate measurement with wearable watch based on PPG. In: IET Conference Publications. 2015. https://doi.org/10.1049/cp.2015.0784.

  16. Eklund JM, Khan N. A bio-signal computing platform for real-time online health analytics for manned space missions. IEEE Aerospace Conf Proceed. 2018. https://doi.org/10.1109/AERO.2018.8396819.

    Article  Google Scholar 

  17. Khan N, Mikael Eklund J. A highly integrated computing platform for continuous, non-invasive BP estimation. Canadian Conf Electr Comput Eng. 2018. https://doi.org/10.1109/CCECE.2018.8447680.

    Article  Google Scholar 

  18. Ghosh S, Banerjee A, Ray N, Wood PW, Boulanger P, Padwal R. Continuous blood pressure prediction from pulse transit time using ECG and PPG signals. In: 2016 IEEE Healthcare Innovation Point-of-Care Technologies Conference, HI-POCT 2016. 2016. https://doi.org/10.1109/HIC.2016.7797728

  19. Shriram R, Wakankar A, Daimiwal N, Ramdasi D. Continuous cuffless blood pressure monitoring based on PTT. In: ICBBT 2010 - 2010 International Conference on Bioinformatics and Biomedical Technology. 2010. https://doi.org/10.1109/ICBBT.2010.5479013.

  20. Gayathri B, Sruthi K, Menon KAU. Non-invasive blood glucose monitoring using near infrared spectroscopy. In: Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017. 2018. https://doi.org/10.1109/ICCSP.2017.8286555.

  21. Paul B, Manuel MP, Alex ZC. Design and development of non invasive glucose measurement system,.In: Proceedings - ISPTS-1, 1st International Symposium on Physics and Technology of Sensors. 2012. https://doi.org/10.1109/ISPTS.2012.6260873.

  22. Makovec M, Aljančič U, Vrtačnik D. Evaluation of chronic venous insufficiency with PPG prototype instrument. In: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 – Proceedings. 2017. https://doi.org/10.23919/MIPRO.2017.7973445.

  23. Bhooma G, Kokila S, Jayanthi KK, Kumar VJ. A digital instrument for venous muscle pump test. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference. 2011. https://doi.org/10.1109/IMTC.2011.5944063

  24. Khan M, Pretty CG, Amies AC, Elliott RB, Suhaimi FM, Shaw GM, Chase JG. Peripheral venous blood oxygen saturation can be non-invasively estimated using photoplethysmography. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2015. https://doi.org/10.1109/EMBC.2015.7319858.

  25. Clarke GWJ, Chan ADC, Adler A. Effects of motion artifact on the blood oxygen saturation estimate in pulse oximetry. In: IEEE MeMeA 2014 - IEEE International Symposium on Medical Measurements and Applications, Proceedings. 2014. https://doi.org/10.1109/MeMeA.2014.6860071.

  26. Shaltis P, Wood L, Reisner A, Asada H. Novel design for a wearable, rapidly deployable, wireless noninvasive triage sensor. Ann Int Conf IEEE Eng Med Biol - Proceed. 2005. https://doi.org/10.1109/iembs.2005.1617250.

    Article  Google Scholar 

  27. Gibbs P, Asada HH. Reducing motion artifact in wearable bio-sensors using MEMS accelerometers for active noise cancellation. Proceed Am Control Conf. 2005. https://doi.org/10.1109/acc.2005.1470193.

    Article  Google Scholar 

  28. Selvi MB, Rajalakshmi S. Extraction and analysis of transabdominal fetal heart rate by NIR Photoplethysmography, Int J Electr Electron Eng. 2015;7. http://www.arresearchpublication.com/images/shortpdf/1427210238_1046.pdf, https://www.semanticscholar.org/paper/EXTRACTION-AND-ANALYSIS-OF-TRANSABDOMINAL-FETAL-BY-Selvi-Rajalakshmi/96730fbd589c3be903ee7aa8baf2bfb18eabc44c.

  29. Gan KB, Zahedi E, Ali MAM. Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results. IEEE Trans Biomed Eng. 2009;56:2075–82. https://doi.org/10.1109/TBME.2009.2021578.

    Article  PubMed  Google Scholar 

  30. Sattar RRM, Chellappan K, Aminuddin A, Omar N, Zakaria Z, Ali MA, Nordin NAMM. Correlation between lipid profile and finger photoplethysmogram morphological properties among young men with cardiovascular risk: a preliminary result. In: IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: “Miri, Where Engineering in Medicine and Biology and Humanity Meet”. 2014. https://doi.org/10.1109/IECBES.2014.7047574.

  31. Zahedi E, Chellappan K, Ali MAM, Singh H. Analysis of the effect of ageing on rising edge characteristics of the photoplethysmogram using a modified windkessel model. Cardiovasc Eng. 2007;7. https://doi.org/10.1007/s10558-007-9037-5.

  32. Wang L, Pickwell-MacPherson E, Liang YP, Zhang YT. Noninvasive cardiac output estimation using a novel photoplethysmogram index. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. 2009. https://doi.org/10.1109/IEMBS.2009.5333091.

  33. Daimiwal N, Sundhararajan M. Non invasive measurement and analysis of cardiac output for different age group using PPG sensor. 2016. https://www.ijcaonline.org/proceedings/ncece2016/number1/24663-9518.

  34. Jönsson B, Laurent C, Eneling M, Skau T, Lindberg LG. Automatic ankle pressure measurements using PPG in ankle-brachial pressure index determination. European J Vasc Endovasc Surg. 2005;30. https://doi.org/10.1016/j.ejvs.2005.05.012.

  35. Karlen W, Raman S, Ansermino JM, Dumont GA. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Trans Biomed Eng. 2013;60. https://doi.org/10.1109/TBME.2013.2246160.

  36. Karlen W, Ansermino JM, Dumont G. Adaptive pulse segmentation and artifact detection in photoplethysmography for mobile applications, In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2012. https://doi.org/10.1109/EMBC.2012.6346628.

  37. Athaya T, Choi S. An estimation method of continuous non-invasive arterial blood pressure waveform using photoplethysmography: a u-net architecture-based approach. Sensors. 2021;21. https://doi.org/10.3390/s21051867.

  38. Chowdhury MH, Shuzan MNI, Chowdhury MEH, Mahbub ZB, Monir Uddin M, Khandakar A, Reaz MBI. Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors (Switzerland). 2020;20. https://doi.org/10.3390/s20113127.

  39. Ghosh A, Kalra J. Schr¨odinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative, biomedical signal processing and control. Elsevier. 2023.

  40. Samimi H, Dajani HR. A PPG-based calibration-free cuffless blood pressure estimation method using cardiovascular dynamics. Sensors. 2023;23. https://doi.org/10.3390/s23084145.

  41. Ibtehaz N, Mahmud S, Chowdhury MEH, Khandakar A, Salman Khan M, Ayari MA, Tahir AM, Rahman MS. PPG2ABP: translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms. Bioengineering. 2022;9. https://doi.org/10.3390/bioengineering9110692.

  42. Paviglianiti A, Randazzo V, Villata S, Cirrincione G, Pasero E. A comparison of deep learning techniques for arterial blood pressure prediction. Cognit Comput. 2022;14. https://doi.org/10.1007/s12559-021-09910-0.

  43. Mehrabadi MA, Aqajari SAH, Zargari AHA, Dutt N, Rahmani AM. Novel blood pressure waveform reconstruction from photoplethysmography using cycle generative adversarial networks. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2022. https://doi.org/10.1109/EMBC48229.2022.9871962.

  44. Samimi H, Dajani HR. Cuffless blood pressure estimation using calibrated cardiovascular dynamics in the photoplethysmogram. Bioengineering. 2022;9. https://doi.org/10.3390/bioengineering9090446.

  45. Harfiya LN, Chang CC, Li YH. Continuous blood pressure estimation using exclusively photopletysmography by lstm-based signal-to-signal translation. Sensors. 2021;21. https://doi.org/10.3390/s21092952.

  46. Yang S, Sohn J, Lee S, Lee J, Kim HC. Estimation and validation of arterial blood pressure using photoplethysmogram morphology features in conjunction with pulse arrival time in large open databases. IEEE J Biomed Health Inform. 2021;25. https://doi.org/10.1109/JBHI.2020.3009658.

  47. Qin K, Huang W, Zhang T. Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal. Biomed Signal Process Control. 2021;70. https://doi.org/10.1016/j.bspc.2021.102972.

  48. Maqsood S, Xu S, Springer M, Mohawesh R. A benchmark study of machine learning for analysis of signal feature extraction techniques for blood pressure estimation using photoplethysmography (PPG). IEEE Access. 2021;9. https://doi.org/10.1109/ACCESS.2021.3117969.

  49. Lee D, Kwon H, Son D, Eom H, Park C, Lim Y, Seo C, Park K. Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network. Sensors (Switzerland). 2021;21. https://doi.org/10.3390/s21010096.

  50. El-Hajj C, Kyriacou PA. Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism. Biomed Signal Process Control. 2021;65. https://doi.org/10.1016/j.bspc.2020.102301.

  51. Hsu YC, Li YH, Chang CC, Harfiya LN. Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only. Sensors (Switzerland). 2020;20. https://doi.org/10.3390/s20195668.

  52. Eom H, Lee D, Han S, Sun Hariyani Y, Lim Y, Sohn I, Park K, Park C. End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism. Sensors (Switzerland). 2020;20. https://doi.org/10.3390/s20082338.

  53. Hasanzadeh N, Ahmadi MM, Mohammadzade H. Blood pressure estimation using photoplethysmogram signal and its morphological features. IEEE Sens J. 2020;20. https://doi.org/10.1109/JSEN.2019.2961411.

  54. Mousavi SS, Firouzmand M, Charmi M, Hemmati M, Moghadam M, Ghorbani Y. Blood pressure estimation from appropriate and inappropriate PPG signals using a whole-based method, Biomed Signal Process Control. 2019;47. https://doi.org/10.1016/j.bspc.2018.08.022.

  55. Slapni Č Ar G, Mlakar N, Luštrek M. Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network. Sensors (Switzerland). 2019;19. https://doi.org/10.3390/s19153420.

  56. Wu D, Xu L, Zhang R, Zhang H, Ren L, Zhang Y-T. Continuous cuff-less blood pressure estimation based on combined information using deep learning approach, J Med Imaging Health Inform. 2018;8. https://doi.org/10.1166/jmihi.2018.2474.

  57. Su P, Ding XR, Zhang YT, Liu J, Miao F, Zhao N. Long-term blood pressure prediction with deep recurrent neural networks. In: 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. 2018. https://doi.org/10.1109/BHI.2018.8333434.

  58. Zadi SA, Alex R, Zhang R, Watenpaugh DE, Behbehani K. Arterial blood pressure feature estimation using photoplethysmography. Comput Biol Med. 2018;102. https://doi.org/10.1016/j.compbiomed.2018.09.013.

  59. Kachuee M, Kiani MM, Mohammadzade H, Shabany M. Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Trans Biomed Eng. 2017;64. https://doi.org/10.1109/TBME.2016.2580904.

  60. Liu M, Po L-M, Fu H. Cuffless blood pressure estimation based on photoplethysmography signal and its second derivative. Int J Comput Theory Eng. 2017;9:202–6. https://doi.org/10.7763/IJCTE.2017.V9.1138.

    Article  Google Scholar 

  61. Zhang Y, Feng Z. A SVM method for continuous blood pressure estimation from a PPG signal. ACM Int Conf Proceed Ser. 2017. https://doi.org/10.1145/3055635.3056634.

    Article  Google Scholar 

  62. Xing X, Sun M. Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. Biomed Opt Express. 2016;7. https://doi.org/10.1364/boe.7.003007.

  63. Kachuee M, Kiani MM, Mohammadzade H, Shabany M. Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time. Proceed - IEEE Int Symp Circ Syst. 2015. https://doi.org/10.1109/ISCAS.2015.7168806.

    Article  Google Scholar 

  64. Kurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation from a PPG signal. Conf Record - IEEE Instrum Meas Technol Conf. 2013. https://doi.org/10.1109/I2MTC.2013.6555424.

    Article  Google Scholar 

  65. Cattivelli FS, Garudadri H. Noninvasive cuffless estimation of blood pressure from pulse arrival time and heart rate with adaptive calibration. In: Proceedings - 2009 6th International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2009. 2009. https://doi.org/10.1109/BSN.2009.35.

  66. Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman LW, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG. Multiparameter intelligent monitoring in intensive care II: a public-access intensive care unit database. Crit Care Med. 2011. https://doi.org/10.1097/CCM.0b013e31820a92c6.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3. https://doi.org/10.1038/sdata.2016.35.

  68. Liang Y, Chen Z, Liu G, Elgendi M. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Sci Data. 2018;5:180020. https://doi.org/10.1038/sdata.2018.20.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Khan M, Kumar Singh B, Nirala N. Expert diagnostic system for detection of hypertension and diabetes mellitus using discrete wavelet decomposition of photoplethysmogram signal and machine learning technique. Med Nov Technol Devices. 2023;19:100251. https://doi.org/10.1016/j.medntd.2023.100251.

    Article  Google Scholar 

  70. Hu X, Yin S, Zhang X, Menon C, Fang C, Chen Z, Elgendi M, Liang Y. Blood pressure stratification using photoplethysmography and light gradient boosting machine. Front Physiol. 2023;14. https://doi.org/10.3389/fphys.2023.1072273.

  71. Islam SMS, Talukder A, Awal MA, Siddiqui MMU, Ahamad MM, Ahammed B, Rawal LB, Alizadehsani R, Abawajy J, Laranjo L, Chow CK, Maddison R. Machine learning approaches for predicting hypertension and its associated factors using population-level data from three South Asian countries. Front Cardiovasc Med. 2022;9. https://doi.org/10.3389/fcvm.2022.839379.

  72. Martinez-Ríos E, Montesinos L, Alfaro-Ponce M. A machine learning approach for hypertension detection based on photoplethysmography and clinical data. Comput Biol Med. 2022;145. https://doi.org/10.1016/j.compbiomed.2022.105479.

  73. Sun X, Zhou L, Chang S, Liu Z. Using cnn and hht to predict blood pressure level based on photoplethysmography and its derivatives, Biosensors (Basel). 2021;11. https://doi.org/10.3390/bios11040120.

  74. Sannino G, De Falco I, De Pietro G. Non-invasive risk stratification of hypertension: a systematic comparison of machine learning algorithms. J Sensor Actuator Networks. 2020;9. https://doi.org/10.3390/JSAN9030034.

  75. Liang Y, Chen Z, Ward R, Elgendi M. Hypertension assessment via ECG and PPG signals: an evaluation using MIMIC database. Diagnostics. 2018;8. https://doi.org/10.3390/diagnostics8030065.

  76. Liang Y, Chen Z, Ward R, Elgendi M. Photoplethysmography and deep learning: enhancing hypertension risk stratification. Biosensors (Basel). 2018;8. https://doi.org/10.3390/bios8040101.

Download references

Acknowledgements

We are deeply grateful for the financial support provided by Quantlase Lab LLC Abu Dhabi, UAE. We would also like to express our gratitude to the management of Quantlase Lab for their visionary leadership and encouragement. We feel deeply indebted to Mr. Sheraz Raza Siddiqui, Executive Director, Quantlase Lab LLC Abu Dhabi for his guidance, advice, and encouragement throughout the period of this research. We would like to extend our sincere gratitude to Dr. Azam Ali Khan and Dr. Nuzhat Ahsan, Quantlase Lab, for the fruitful discussions during the preparation of this manuscript. We extend our sincere appreciation to the entire team at Quantlase Lab for their collaborative spirit and dedication.

Author information

Authors and Affiliations

  1. Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates

    Javed Alam, Mohammad Firoz Khan, Meraj Alam Khan, Rinky Singh, Mohammed Mundazeer & Pramod Kumar

  2. DigiBiomics Inc, Mississauga, Ontario, Canada

    Meraj Alam Khan

Authors
  1. Javed Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Firoz Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meraj Alam Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rinky Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammed Mundazeer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pramod Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javed Alam.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Associate Editor Marat Fudim oversaw the review of this article

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alam, J., Khan, M.F., Khan, M.A. et al. A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG). J. of Cardiovasc. Trans. Res. (2023). https://doi.org/10.1007/s12265-023-10462-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12265-023-10462-x

Keywords

Search

Navigation