Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring

AbstractHeart rate variability (HRV) analysis represents an important tool for the characterization of complex cardiovascular control. HRV indexes are usually calculated from electrocardiographic (ECG) recordings after measuring the time duration between consecutive R peaks, and this is considered the gold standard. An alternative method consists of assessing the pulse rate variability (PRV) from signals acquired through photoplethysmography, a technique also employed for the continuous noninvasive monitoring of blood pressure. In this work, we carry out a thorough analysis and comparison of short-term variability indexes computed from HRV time series obtained from the ECG and from PRV time series obtained from continuous blood pressure (CBP) signals, in order to evaluate the reliability of using CBP-based recordings in place of standard ECG tracks. The analysis has been carried out on short time series (300 beats) of HRV and PRV in 76 subjects studied in different conditions: resting in the supine position, postural stress during 45° head-up tilt, and mental stress during computation of arithmetic test. Nine different indexes have been taken into account, computed in the time domain (mean, variance, root mean square of the successive differences), frequency domain (low-to-high frequency power ratio LF/HF, HF spectral power, and central frequency), and information domain (entropy, conditional entropy, self entropy). Thorough validation has been performed using comparison of the HRV and PRV distributions, robust linear regression, and Bland–Altman plots. Results demonstrate the feasibility of extracting HRV indexes from CBP-based data, showing an overall relatively good agreement of time-, frequency-, and information-domain measures. The agreement decreased during postural and mental arithmetic stress, especially with regard to band-power ratio, conditional, and self-entropy. This finding suggests to use caution in adopting PRV as a surrogate of HRV during stress conditions.

[1]  Luca Faes,et al.  Are Nonlinear Model-Free Conditional Entropy Approaches for the Assessment of Cardiac Control Complexity Superior to the Linear Model-Based One? , 2017, IEEE Transactions on Biomedical Engineering.

[2]  Survi Kyal,et al.  Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice , 2015, IEEE Transactions on Biomedical Engineering.

[3]  Jong Yong Abdiel Foo,et al.  Pulse transit time as an indirect marker for variations in cardiovascular related reactivity. , 2006, Technology and health care : official journal of the European Society for Engineering and Medicine.

[4]  Rodrigo Varejão Andreão,et al.  Spectral analysis of heart rate variability with the autoregressive method: What model order to choose? , 2012, Comput. Biol. Medicine.

[5]  Nigel H. Lovell,et al.  Change in pulse transit time and pre-ejection period during head-up tilt-induced progressive central hypovolaemia , 2007, Journal of Clinical Monitoring and Computing.

[6]  G. Baselli,et al.  Spectral decomposition in multichannel recordings based on multivariate parametric identification , 1997, IEEE Transactions on Biomedical Engineering.

[7]  F. Shaffer,et al.  An Overview of Heart Rate Variability Metrics and Norms , 2017, Front. Public Health.

[8]  X. Aubert,et al.  Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population? , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[9]  Omer T. Inan,et al.  Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time , 2016, Scientific Reports.

[10]  T. Walther,et al.  Comparison of three methods for beat-to-beat-interval extraction from continuous blood pressure and electrocardiogram with respect to heart rate variability analysis. , 2006, Biomedizinische Technik. Biomedical engineering.

[11]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[12]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[13]  K H Wesseling Finger arterial pressure measurement with Finapres. , 1996, Zeitschrift fur Kardiologie.

[14]  P. Laguna,et al.  Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions , 2010, Physiological measurement.

[15]  Giuseppe Baselli,et al.  Measuring regularity by means of a corrected conditional entropy in sympathetic outflow , 1998, Biological Cybernetics.

[16]  B. Porat,et al.  Digital Spectral Analysis with Applications. , 1988 .

[17]  A. Porta,et al.  Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt. , 1994, Circulation.

[18]  J. Naschitz,et al.  Pulse Transit Time by R-Wave-Gated Infrared Photoplethysmography: Review of the Literature and Personal Experience , 2004, Journal of Clinical Monitoring and Computing.

[19]  L. Mistretta,et al.  Physiological parameters measurements in a cardiac cycle via a combo PPG-ECG system , 2015, 2015 AEIT International Annual Conference (AEIT).

[20]  S. Huffel,et al.  Instantaneous changes in heart rate regulation due to mental load in simulated office work , 2011, European Journal of Applied Physiology.

[21]  J. Andrew Taylor,et al.  The physiological basis and measurement of heart rate variability in humans , 2016, Journal of Physiological Anthropology.

[22]  J. Vagedes,et al.  How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. , 2013, International journal of cardiology.

[23]  H. Malberg,et al.  Comparison of three methods for beat-to-beat-interval extraction from continuous blood pressure and electrocardiogram with respect to heart rate variability analysis / Vergleich von drei Methoden der Schlag-zu-Schlag-Intervall-Extraktion aus kontinuierlichen Blutdruckverläufen und Elektrokardiogramm , 2006 .

[24]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[25]  J. Taylor,et al.  Short‐term cardiovascular oscillations in man: measuring and modelling the physiologies , 2002, The Journal of physiology.

[26]  Emilia Bagiella,et al.  Deriving heart period variability from blood pressure waveforms. , 2003, Journal of applied physiology.

[27]  K. Wesseling,et al.  Fifteen years experience with finger arterial pressure monitoring: assessment of the technology. , 1998, Cardiovascular research.

[28]  Xiang Chen,et al.  Effect of changes in sympathovagal balance on the accuracy of heart rate variability obtained from photoplethysmography. , 2015, Experimental and therapeutic medicine.

[29]  M Javorka,et al.  Preejection period as a sympathetic activity index: a role of confounding factors. , 2017, Physiological research.

[30]  Luca Faes,et al.  Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations. , 2017, Physical review. E.

[31]  Heather T. Ma,et al.  Spectral Analysis of Pulse Transit Time Variability and Its Coherence with Other Cardiovascular Variabilities , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  F. Shaffer,et al.  Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health risk , 2015, Global advances in health and medicine.

[33]  R. González,et al.  Comparison of the heart rate variability parameters obtained from the electrocardiogram and the blood pressure wave. , 1998, Journal of medical engineering & technology.

[34]  N. Montano,et al.  Complexity and Nonlinearity in Short-Term Heart Period Variability: Comparison of Methods Based on Local Nonlinear Prediction , 2007, IEEE Transactions on Biomedical Engineering.

[35]  Luca Faes,et al.  Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks , 2016, Entropy.

[36]  Álvaro Alesanco Iglesias,et al.  Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects , 2018, Sensors.

[37]  Jonathon P. Leider,et al.  The Double Disparity Facing Rural Local Health Departments. , 2016, Annual review of public health.

[38]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[39]  D. Giavarina Understanding Bland Altman analysis , 2015, Biochemia medica.

[40]  Yu Sun,et al.  Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging , 2016, IEEE Transactions on Biomedical Engineering.

[41]  Luca Faes,et al.  Towards understanding the complexity of cardiovascular oscillations: Insights from information theory , 2018, Comput. Biol. Medicine.

[42]  Zhi-Hong Mao,et al.  Cuff-free blood pressure estimation using pulse transit time and heart rate , 2014, 2014 12th International Conference on Signal Processing (ICSP).

[43]  Westgate Road,et al.  Photoplethysmography and its application in clinical physiological measurement , 2007 .

[44]  D. O'Connor,et al.  Cardiovascular haemodynamic response to repeated mental stress in normotensive subjects at genetic risk of hypertension: evidence of enhanced reactivity, blunted adaptation, and delayed recovery , 2003, Journal of Human Hypertension.

[45]  Riccardo Pernice,et al.  Reliability of Short-Term Heart Rate Variability Indexes Assessed through Photoplethysmography , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[46]  Alberto Porta,et al.  Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices , 2011, Physiological measurement.

[47]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[48]  C. Peng,et al.  Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance–based therapy , 2007, Europhysics letters.

[49]  L Faes,et al.  Univariate and multivariate conditional entropy measures for the characterization of short-term cardiovascular complexity under physiological stress , 2018, Physiological measurement.

[50]  Wesseling Kh Finger arterial pressure measurement with Finapres. , 1996 .

[51]  Zoltán Gingl,et al.  Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera , 2018, Journal of healthcare engineering.

[52]  Zhiquan Feng,et al.  A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera , 2016, Journal of Medical Systems.

[53]  A. Porta,et al.  Progressive decrease of heart period variability entropy-based complexity during graded head-up tilt. , 2007, Journal of applied physiology.

[54]  Potter,et al.  Should one use electrocardiographic or Finapres-derived pulse intervals for calculation of cardiac baroreceptor sensitivity? , 1998, Blood pressure monitoring.

[55]  Marcus Vollmer,et al.  A robust, simple and reliable measure of heart rate variability using relative RR intervals , 2015, 2015 Computing in Cardiology Conference (CinC).

[56]  G. Lu,et al.  A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects , 2009, Journal of medical engineering & technology.

[57]  Niels Wessel,et al.  Addressing the complexity of cardiovascular regulation , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[58]  Marimuthu Palaniswami,et al.  Comparison of pulse rate variability with heart rate variability during obstructive sleep apnea. , 2011, Medical engineering & physics.

[59]  J. Saul,et al.  Transfer function analysis of the circulation: unique insights into cardiovascular regulation. , 1991, The American journal of physiology.

[60]  Robert Rauh,et al.  Comparison of heart rate variability and pulse rate variability detected with photoplethysmography , 2004, Saratov Fall Meeting.

[61]  L. Mainardi,et al.  Relationships between heart-rate variability and pulse-rate variability obtained from video-PPG signal using ZCA , 2016, Physiological measurement.

[62]  L. Faes,et al.  Investigating the mechanisms of cardiovascular and cerebrovascular regulation in orthostatic syncope through an information decomposition strategy , 2013, Autonomic Neuroscience.

[63]  A. Tomasino,et al.  PPG embedded system for blood pressure monitoring , 2014, 2014 AEIT Annual Conference - From Research to Industry: The Need for a More Effective Technology Transfer (AEIT).

[64]  K. Chon,et al.  Can Photoplethysmography Variability Serve as an Alternative Approach to Obtain Heart Rate Variability Information? , 2008, Journal of Clinical Monitoring and Computing.

[65]  Luca Faes,et al.  Estimating the decomposition of predictive information in multivariate systems. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[66]  R. Edelberg,et al.  Comparison of finger plethysmograph to ECG in the measurement of heart rate variability. , 2002, Psychophysiology.

[67]  A. Porta,et al.  A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients , 2015, Entropy.

[68]  Viola Priesemann,et al.  Bits from Brains for Biologically Inspired Computing , 2014, Front. Robot. AI.

[69]  Luca Faes,et al.  Basic cardiovascular variability signals: mutual directed interactions explored in the information domain , 2017, Physiological measurement.