{"id":56859,"date":"2024-03-20T10:32:07","date_gmt":"2024-03-20T10:32:07","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=56859"},"modified":"2024-04-02T04:21:31","modified_gmt":"2024-04-02T04:21:31","slug":"assessing-heart-rate-variability-and-pulse-rate-variability-patterns-in-cardiac-patients-exploring-the-utility-of-photoplethysmography-and-electrocardiography","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no1\/assessing-heart-rate-variability-and-pulse-rate-variability-patterns-in-cardiac-patients-exploring-the-utility-of-photoplethysmography-and-electrocardiography\/","title":{"rendered":"Assessing Heart Rate Variability and Pulse Rate Variability Patterns in Cardiac Patients: Exploring the Utility of Photoplethysmography and Electrocardiography"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Over twenty years ago, a working committee of the European Society\nof Cardiology and the North American Society of Pacing and Electrophysiology established\nheart rate variability as the period between two successive heartbeats1<\/sup>. HRV has been used to establish a specific cardiac vagal tone\nindex for the parasympathetic nervous system2,3<\/sup>. Heart rate variability (HRV) has emerged as a noninvasive marker\nof autonomic nervous system (ANS) activity, reflecting the dynamic balance\nbetween sympathetic and parasympathetic influences on the heart. HRV analysis\nis fascinating in human performance and health monitoring, where it has been\nlinked to various physiological and psychological states, including stress,\nfatigue, and recovery4<\/sup>. Many factors influence measures of heart rate variability, such\nas age, obesity, and postural changes, resulting in altered autonomic nervous\nsystem tone and stress index measures5<\/sup>. <\/p>\n\n\n\n

Additionally, HRV is a straightforward, affordable, and noninvasive\nassessment that appeals to many cardiology and psychophysiology researchers.\nThe interbeat interval (IBI), photoplethysmography, and electrocardiogram (ECG)\nare methods of measuring heart rate variability (HRV)6<\/sup>. While ECG recordings have higher accuracy and are more helpful in\ndetecting ORS or R peaks and adjusting for electrode errors, traditional\ndevices use them to estimate heart rate variables7<\/sup> 8<\/sup>. The\ntime between heartbeats is estimated utilizing polar heart rate or chest belt\nrecords equipment because contemporary technologies use IBI to assess HRV9<\/sup>. These models’ major problems are inaccurate IBI detection and\nartifacts brought on by skin motion, as well as the fact that they only use IBI\nand not accurate ECG signal, R-wave, or QRS detection10<\/sup>.<\/p>\n\n\n\n

Regarding a method of measuring pulse rate variability (PRV), photoplethysmography\n(PPG) digitizes observations of absorbed light, which changes with periodic circulation\nin the arteries11<\/sup>. In a few earlier studies, an association between HRV and PRV\nhas been shown12<\/sup> 13<\/sup>. According to several studies, a smartphone or inexpensive\nequipment might also be used by anyone to measure PRV rapidly 14<\/sup> 15<\/sup>. PPG\nhas experienced a revival in recent decades thanks to developments in\noptoelectronics and digital signal processing, and it is now likely the most\nwidely utilized technique in clinical monitoring. PPG technology has the\nadvantages of being non-intrusive, affordable, and simple to use 6<\/sup>.<\/p>\n\n\n\n

An oximeter for pulse (PO) is available by\ndefault in medical centers, and the analysis of HRV in the surveillance process\nwithout necessitating an electrocardiogram (ECG) has a significant benefit.\nOther than for MRI (magnetic resonance imaging), it is prohibited to use ECG\nelectrodes or other metal-containing sensors because they can interfere with\npowerful electromagnetic fields. Compared to an ECG, which typically requires\nat least three leads and electrodes, the PPG signal can often be detected by\njust placing a single sensor on a finger or earlobe. These electrocardiogram\nelectrodes frequently need to be put in the chest, necessitating patients to\nundress and presenting a problem for patients16<\/sup>.<\/p>\n\n\n\n

Therefore, the aim is to investigate the correlation between HRV\nfor the ECG signal and PRV in a cardiac patient.<\/p>\n\n\n\n

Material and\nmethod<\/strong><\/p>\n\n\n\n

Data<\/strong><\/p>\n\n\n\n

The data in this investigation included the physiological signals\nof 53 patients (21 men and 32 women) aged between 19-96 from the PhysioNet Dataset.\nThe Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals records are\nabout 8 minutes, and the sampling rate is 125 hertz (Hz). Data and patient\nnotations were collected from critically ill patients during their\nhospitalization at Beth Israel Deaconess Medical Centre (Boston, MA, USA) 17<\/sup> 18<\/sup>. <\/p>\n\n\n\n

Analysis method<\/strong><\/p>\n\n\n\n

After obtaining the data containing many signals, such as the ECG\nwith three leads (II, V1, V5), PPG, and respiratory impedance signal, two\nsignals were chosen: lead II, because it gives a higher peak than the other\nleads for the ECG signal, and the PPG signal to use in the HRV analysis. Then,\nusing the Acqknowledge 5.0 software, which was attached to the BioPac system\n(MP160, USA), HRV parameters were produced together with estimates of the PRV\nfrom the PPG signal, and the R-R interval was calculated independently for the\nECG signal. For HRV analysis, the linear mode was used with the time and\nfrequency domains. The standard deviation of (RR, PP) interval (SDNN), root\nmean square of (RR, PP) interval adjacent (RMSSD), number of (RR, PP) upper\n50ms (NN50 COUNT), proportion of NN50 (PNN50%), power spectral of low frequency\n(LF in ms), high frequency (HF in ms), normalized unite for LFnu, HFnu. The\nproportion of LF\/HF for the time and frequency domains was calculated,\nrespectively.<\/p>\n\n\n\n

Statical analyzing<\/strong><\/p>\n\n\n\n

The data was statistically analyzed using SPSS software version 26.\nFor continuous variables, the mean and standard deviation are provided. The\nvariances in measurements were compared using a paired-sample t-test. The\nBland-Altman approach and standard linear regression were used to test the\nagreement method, and the coefficient of correlation (CC) was calculated using\nthe Spearman rank correlation method. It was decided to use a correlation\ncoefficient to denote a suitable level of relationship 19<\/sup> 20<\/sup>. A p-value<\/em> was chosen (p< 0.05).<\/p>\n\n\n\n

Result<\/strong><\/p>\n\n\n\n

According to the results obtained, such as shown in Table 1 and\nfigures, firstly, from the calculation of RR and PP intervals, we find that\nboth signals provide close values for all parameters of HRV.<\/p>\n\n\n\n

Table 1: The linear parameter of the Heart Rate Variability for the two signals ECG, PPG<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 Variable<\/strong><\/p>\n<\/td>\n

\n

Electrocardiogram Signals<\/strong><\/p>\n<\/td>\n

\n

Photoplethysmogram<\/strong><\/p>\n

Signals<\/strong><\/p>\n<\/td>\n

\n

Correlation coefficient(r)<\/strong><\/p>\n<\/td>\n

\n

p<\/em>.value<\/strong><\/p>\n<\/td>\n

\n

CI 95%<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Time domain <\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Mean PP, RR (ms)<\/p>\n<\/td>\n

\n

695.6 \u00b1 112.9<\/p>\n<\/td>\n

\n

694.4 \u00b1 112.9<\/p>\n<\/td>\n

\n

0.998<\/p>\n<\/td>\n

\n

0.163<\/p>\n<\/td>\n

\n

-0.46 to 2.66<\/p>\n<\/td>\n<\/tr>\n

\n

Mean HR (Beats)<\/p>\n<\/td>\n

\n

89.2 \u00b1 13.5<\/p>\n<\/td>\n

\n

89.5 \u00b1 13.8<\/p>\n<\/td>\n

\n

0.992<\/p>\n<\/td>\n

\n

0.267<\/p>\n<\/td>\n

\n

-0.72 to 0.20<\/p>\n<\/td>\n<\/tr>\n

\n

RMSSD (ms)<\/p>\n<\/td>\n

\n

76.1 \u00b1 99.4<\/p>\n<\/td>\n

\n

74.9 \u00b1 92.2<\/p>\n<\/td>\n

\n

0.995<\/p>\n<\/td>\n

\n

0.894<\/p>\n<\/td>\n

\n

-0.66 to 0.58<\/p>\n<\/td>\n<\/tr>\n

\n

SDNN (ms)<\/p>\n<\/td>\n

\n

54.3 \u00b1 61.8<\/p>\n<\/td>\n

\n

54.5 \u00b1 62.1<\/p>\n<\/td>\n

\n

0.999<\/p>\n<\/td>\n

\n

0.321<\/p>\n<\/td>\n

\n

-0.58 to 0.19<\/p>\n<\/td>\n<\/tr>\n

\n

NN50<\/p>\n<\/td>\n

\n

84 \u00b1 144.6<\/p>\n<\/td>\n

\n

89.4 \u00b1 140.1<\/p>\n<\/td>\n

\n

0.999<\/p>\n<\/td>\n

\n

0.11<\/p>\n<\/td>\n

\n

-0.8 to 0.08<\/p>\n<\/td>\n<\/tr>\n

\n

pNN50 (%)<\/p>\n<\/td>\n

\n

14.9 \u00b1 24.8<\/p>\n<\/td>\n

\n

12.8 \u00b1 22.6<\/p>\n<\/td>\n

\n

0.995<\/p>\n<\/td>\n

\n

0.465<\/p>\n<\/td>\n

\n

-0.13 to 0.27<\/p>\n<\/td>\n<\/tr>\n

\n

Frequency Domain <\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

LF Power (ms2<\/sup>)<\/p>\n<\/td>\n

\n

1328.7 \u00b1 3619.9<\/p>\n<\/td>\n

\n

1350.3 \u00b1 3590.50<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0.056<\/p>\n<\/td>\n

\n

-1.19 to 0.016<\/p>\n<\/td>\n<\/tr>\n

\n

HF Power (ms2<\/sup>)<\/p>\n<\/td>\n

\n

2687.6 \u00b1 6851.4<\/p>\n<\/td>\n

\n

2610.9 \u00b1 6852<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

0.452<\/p>\n<\/td>\n

\n

-1.27 to 0.58<\/p>\n<\/td>\n<\/tr>\n

\n

LF nu<\/p>\n<\/td>\n

\n

36.4 \u00b1 22.1<\/p>\n<\/td>\n

\n

33.6 \u00b1 24<\/p>\n<\/td>\n

\n

0.999<\/p>\n<\/td>\n

\n

0.084<\/p>\n<\/td>\n

\n

-0.46 to 0.030<\/p>\n<\/td>\n<\/tr>\n

\n

HF nu<\/p>\n<\/td>\n

\n

63.2 \u00b1 21.9<\/p>\n<\/td>\n

\n

69.1 \u00b1 21.8<\/p>\n<\/td>\n

\n

0.998<\/p>\n<\/td>\n

\n

0.633<\/p>\n<\/td>\n

\n

-0.25 to 0.40<\/p>\n<\/td>\n<\/tr>\n

\n

LF\/HF<\/p>\n<\/td>\n

\n

1.7\u00b1 5.3<\/p>\n<\/td>\n

\n

1.6 \u00b1 4.6<\/p>\n<\/td>\n

\n

0.997<\/p>\n<\/td>\n

\n

0.355<\/p>\n<\/td>\n

\n

-1.17 to 0.32<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Every variable has a p-value greater than 0.05. for all parameters delivered\nfrom the ECG and PPG signals. All parameters either in the frequency domain or\nin the time domain for both signals showed a strong correlation (LFnu, HFnu,\nand proportion of LF\/HF), (RR intervals, SDNN, NN50, RMSSD, and pNN50), (r \u2265\n0.99, p>0.05). Every feature for each patient generated from the ECG and PPG\nexhibits good agreement for every parameter in the Bland-Altman analysis (see\nFigures 1 and 2).<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1<\/strong>: <\/strong>(a) <\/strong>B&A Plot of the average <\/strong>values of the time-domain indices versus their differences, (b) Boxplot of time-domain indices.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 2: (a) B&A Plot of the average of the Frequency-domain indices versus their differences, (b) Boxplot of Frequency-domain indices.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

Two commonly used methods for measuring HRV are electrocardiogram\n(ECG) and photoplethysmography (PPG), which provide distinct measures of\ncardiac activity.<\/p>\n\n\n\n

In this study, we compared HRV analysis using electrocardiogram\n(ECG) and photoplethysmogram (PPG) signals in a sample of patients. We examined\nthe agreement and correlation between various HRV parameters derived from ECG\nand PPG signals, including frequency-domain and time-domain measures. Our\nfindings suggest that both ECG and PPG signals can be used for HRV analysis.<\/p>\n\n\n\n

According to the findings of the current investigation, the pulse\ninterval variability (PIV) and heart rate variability provided by the\nelectrocardiogram (ECG) and photoplethysmogram (PPG) signals show strong agreement\nfor critically ill patients.<\/p>\n\n\n\n

As shown in this investigation, the earlier results presented by C. Kiran Kumar, JA Heathers et al., Gil E et al, and Hayano et al., for healthy participants showed a significant link between pulse rate variability (PRV) and Heart rate variability (HRV). So, it established PRV as an accurate replacement for HRV, which has been utilized to get beyond the confounding effect of breathing of evaluation in HRV parameters15<\/sup> 21<\/sup> 22<\/sup> 23<\/sup>. Moreover, it may be impacted by exercise, stress, changes in hemodynamics, or adjustments in metabolism. In addition, HRV has additional issues with wire density, complicated morphology, drift, and adhesive electrode patches. While little research, including Constant I, 1999, has disputed the preceding consensus 24<\/sup>. <\/p>\n\n\n\n

Clinically significant factors, including heart and respiratory\nrate and respiration-induced intensity fluctuations, can be measured using the\nPPG signal. The usage of PPG has risen in clinical monitoring in both research\nand practice due to recent developments and improvements in digital signal\nprocessing. Modern PPG sensors use inexpensive optoelectronic modules that\noperate in the red or infrared spectrum. Additionally, it is utilized to\ncalculate cardiac output, endothelial and venous function, peripheral arterial\nocclusion, and pulse wave velocity. To highlight the most important results of\nthe Bland-Altman technique, we collected the standard deviation and\nBland-Altman plot for the major HRV indices corresponding to the ECG-PPG\ncomparisons. The indices of both approaches showed significant agreement with\none another. According to a study released by Wong Jih-Sen et al., all Heart\nrate variability (HRV) parameters and the corresponding Pulse rate variability\n(PRV) measures of both hands in each patient showed a significant agreement 25<\/sup>. Therefore, we utilized the left side to provide a more accurate\nand reliable assessment and observed that the PRV and HRV had an acceptable\nlevel of agreement.<\/p>\n\n\n\n

In contrast, several researchers discovered little concordance\nbetween PRV and HRV 24<\/sup>. In various situations, various factors may affect how HRV and PRV\ndiffer. Due to physical differences, mechanical waves created in vessels for\nPRV and electrical waves originating from the heart 26<\/sup>.<\/p>\n\n\n\n

A primary or secondary autonomic failure can be assessed using HRV\nand RVP, which are early indications of ANS dysfunction. They also aid in\nlowering the mortality and morbidity linked to cardiovascular diseases27<\/sup>. They are convenient, noninvasive techniques for diagnosing\nneurological conditions and for use in exercise interventional investigations\nand sports evaluation 28<\/sup>.<\/p>\n\n\n\n

The current study has limits, as it was conducted on a group with a\ndisease to be sure of the result. So, we would like to apply the same study to\na large sample of people to predict their health status as part of a\nprospective study of various diseases, including hemodynamic diseases such as\nhypotension and real-time hospitalized patients, by using PVR parameters.<\/p>\n\n\n\n

Conclusion <\/strong><\/p>\n\n\n\n

The study’s findings suggest that PRV derived from\nPPG-Signal can be as effective as HRV in assessing the autonomic tone of the\nheart and predicting the health status of patients based on the comparison of\ncalculated PRV parameters with HRV parameters in a group of patients with a\nknown medical history.<\/p>\n\n\n\n

Acknowledgment<\/strong><\/p>\n\n\n\n

No Acknowledgment<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

There is no conflict of interests in association\nwith the material presented in this paper.<\/p>\n\n\n\n

Funding Source<\/strong><\/p>\n\n\n\n

This study did not receive any funding.<\/p>\n\n\n\n

References <\/strong><\/p>\n\n\n\n

  1. Malik M. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation<\/em>. 1996;93:1043-1065.
    CrossRef<\/a><\/li>
  2. Li Z, Snieder H, Su S, A longitudinal study in youth of heart rate variability at rest and in response to stress. Int J Psychophysiol<\/em>. 2009;73(3):212-217. doi:10.1016\/j.ijpsycho.2009.03.002
     CrossRef <\/a><\/li>
  3. Laborde S, Mosley E, Thayer JF. Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research \u2013 Recommendations for Experiment Planning, Data Analysis, and Data Reporting. Frontiers in Psychology<\/em>. 2017;8:213. doi:10.3389\/fpsyg.2017.00213
    CrossRef <\/a><\/li>
  4. Farhan A, Lyazidi A, Labakoum B, Rattal M, Mouhsen A. Impact of Heart Rate Variability on Physiological Stress: Systematic Review. Biomed Pharmacol J<\/em>. 2023;16(2):997-1010. doi:10.13005\/bpj\/2681
     CrossRef <\/a><\/li>
  5. Farhan A, Lyazidi A, Elkettani S, Labakoum B, Rattal M, Mouhsen A. Linear Analysis of ECG Data Variability to Assess The Autonomic Nervous System in Two Different Body Positions. The Egyptian Journal of Hospital Medicine<\/em>. 2023;90(1):459-464. doi:10.21608\/ejhm.2023.279661
     CrossRef <\/a><\/li>
  6. Sch\u00e4fer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? International Journal of Cardiology<\/em>. 2013;166(1):15-29. doi:10.1016\/j.ijcard.2012.03.119
     CrossRef <\/a><\/li>
  7. Kleiger RE, Stein PK, Bigger Jr. JT. Heart Rate Variability: Measurement and Clinical Utility. Annals of Noninvasive Electrocardiology<\/em>. 2005;10(1):88-101. doi:10.1111\/j.1542-474X.2005.10101.x
     CrossRef <\/a><\/li>
  8. Munoz ML, Roon A van, Riese H, Validity of (Ultra-)Short Recordings for Heart Rate Variability Measurements. PLOS ONE<\/em>. 2015;10(9):e0138921. doi:10.1371\/journal.pone.0138921
     CrossRef <\/a><\/li>
  9. Farhan A, Rattal M, Labakoum B, Essoukaki E, Mouhsen A, Lyazidi A. Comparison of automatic and manual methods for analyzing heart rate variability parameters. In: 1ST INTERNATIONAL CONFERENCE ON CHEMICAL AND BIOLOGICAL SCIENCES (ICCBS\u201922)<\/em>. Sciendo; 2023:39-44. doi:10.2478\/9788367405256-006
     CrossRef <\/a><\/li>
  10. Jarrin DC, McGrath JJ, Giovanniello S, Poirier P, Lambert M. Measurement fidelity of heart rate variability signal processing: The devil is in the details. Int J Psychophysiol<\/em>. 2012;86(1):88-97. doi:10.1016\/j.ijpsycho.2012.07.004
     CrossRef <\/a><\/li>
  11. Leikan G, Rossi E, Sanz Mc, Evaluation of agreement between temporal series obtained from electrocardiogram and pulse wave. J Phys: Conf Ser<\/em>. 2016;705:012038. doi:10.1088\/1742-6596\/705\/1\/012038
    CrossRef <\/a><\/li>
  12. Georgieva-Tsaneva GN, Gospodinova E. Comparative Heart Rate Variability Analysis of ECG, Holter and PPG Signals. IJACSA<\/em>. 2021;12(12). doi:10.14569\/IJACSA.2021.0121261
     CrossRef <\/a><\/li>
  13. Peng R chao, Zhou X, Lin WH, Zhang YT. Extraction of Heart Rate Variability from Smartphone Photoplethysmograms. Computational and Mathematical Methods in Medicine<\/em>. 2015;2015:1-11. doi:10.1155\/2015\/516826
    CrossRef <\/a><\/li>
  14. Cho Y, Julier SJ, Bianchi-Berthouze N. Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging. JMIR Ment Health<\/em>. 2019;6(4):e10140. doi:10.2196\/10140
     CrossRef <\/a><\/li>
  15. Heathers JAJ. Smartphone-enabled pulse rate variability: An alternative methodology for the collection of heart rate variability in psychophysiological research. International Journal of Psychophysiology<\/em>. 2013;89(3):297-304. doi:10.1016\/j.ijpsycho.2013.05.017
     CrossRef <\/a><\/li>
  16. B\u00e1nhalmi A, Borb\u00e1s J, Fidrich M, Bilicki V, Gingl Z, Rudas L. Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera. Journal of Healthcare Engineering<\/em>. 2018;2018:1-15. doi:10.1155\/2018\/4038034
     CrossRef <\/a><\/li>
  17. Pimentel MAF, Johnson AEW, Charlton PH, Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters. IEEE Transactions on Biomedical Engineering<\/em>. 2017;64(8):1914-1923. doi:10.1109\/TBME.2016.2613124
     CrossRef <\/a><\/li>
  18. Goldberger AL, Amaral LAN, Glass L, PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation<\/em>. 2000;101(23). doi:10.1161\/01.CIR.101.23.e215
     CrossRef <\/a><\/li>
  19. Errotta ANSP, Eklin ANTJ, Ives BENAH, Eanwell LEAHEM, Arburton DAERW. VALIDITY OF THE ELITE HRV SMARTPHONE APPLICATION FOR EXAMINING HEART RATE VARIABILITY IN A FIELD-BASED SETTING. Published online 2017:2296-2302.
     CrossRef <\/a><\/li>
  20. Bunce C. Correlation, Agreement, and Bland-Altman Analysis: Statistical Analysis of Method Comparison Studies. American Journal of Ophthalmology<\/em>. 2009;148(1):4-6. doi:10.1016\/j.ajo.2008.09.032
     CrossRef <\/a><\/li>
  21. Hayano J, Barros AK, Kamiya A, Ohte N, Yasuma F. Assessment of pulse rate variability by the method of pulse frequency demodulation. BioMed Eng OnLine<\/em>. 2005;4(1):62. doi:10.1186\/1475-925X-4-62
     CrossRef <\/a><\/li>
  22. Gil E, Orini M, Bail\u00f3n R, Vergara JM, Mainardi L, Laguna P. Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions. Physiol Meas<\/em>. 2010;31(9):1271-1290. doi:10.1088\/0967-3334\/31\/9\/015
     CrossRef <\/a><\/li>
  23. Kiran kumar C, Manaswini M, Maruthy KN, Siva Kumar AV, Mahesh kumar K. Association of Heart rate variability measured by RR interval from ECG and pulse to pulse interval from Photoplethysmography. Clinical Epidemiology and Global Health<\/em>. 2021;10(December 2020):100698. doi:10.1016\/j.cegh.2021.100698
    CrossRef <\/a><\/li>
  24. Constant I, Laude D, MURAT I, ELGHOZI JL. Pulse rate variability is not a surrogate for heart rate variability. Clinical science (London, England\u202f: 1979)<\/em>. 1999;97:391-397. doi:10.1042\/CS19990062
     CrossRef <\/a><\/li>
  25. Wong JS, Lu WA, Wu KT, Liu M, Chen GY, Kuo CD. A comparative study of pulse rate variability and heart rate variability in healthy subjects. J Clin Monit Comput<\/em>. 2012;26(2):107-114. doi:10.1007\/s10877-012-9340-6
     CrossRef <\/a><\/li>
  26. Mej\u00eda-Mej\u00eda E, Budidha K, Ysehak Abay T, May J. Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses. Frontiers in Physiology<\/em>. 2020;11:779.
     CrossRef <\/a><\/li>
  27. Li X, Hu C, Meng A, Guo Y, Chen Y, Dang R. Heart rate variability and heart rate monitoring of nurses using PPG and ECG signals during working condition: A pilot study. Health Science Reports<\/em>. 2022;5(1). doi:10.1002\/hsr2.477
     CrossRef <\/a><\/li>
  28. Maheshkumar K, Sivakumar AV, Maruthy KN, Padmavathi R. Hot arm and foot bath on heart rate variability and blood pressure in healthy volunteers \u2013 needs to be verified with standard device? Journal of Complementary and Integrative Medicine<\/em>. 2020;9(0):1. doi:10.1515\/jcim-2019-0318
     CrossRef <\/a><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

    Introduction Over twenty years ago, a working committee of the […]<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[113],"tags":[],"class_list":["post-56859","post","type-post","status-publish","format-standard","hentry","category-vol17no1"],"_links":{"self":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/56859","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/comments?post=56859"}],"version-history":[{"count":5,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/56859\/revisions"}],"predecessor-version":[{"id":57490,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/56859\/revisions\/57490"}],"wp:attachment":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/media?parent=56859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/categories?post=56859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/tags?post=56859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}