open access
Validity of the Pneumonitor for RR intervals acquisition for short-term heart rate variability analysis extended with respiratory data in pediatric cardiac patients


- Department of Pediatric Cardiology and General Pediatrics, Medical University of Warsaw, Warszawa, Poland
open access
Abstract
BACKGROUND: Breathing pattern alterations change the variability and the spectral content of the RR intervals (RRi) from electrocardiogram (ECG). However, actually there is no solution on how to record and control participant’s breathing without influencing its natural rate and depth in heart rate variability (HRV) studies.
AIM: The aim of the study was to assess the validity of the Pneumonitor for acquisition of short-term (5 min) RRi in comparison to the reference ECG method for analysis of heart rate (HR) and HRV parameters in the group of pediatric patients with cardiac disease.
METHODS: Nineteen patients of both sexes participated in the study. ECG and Pneumonitor were used to record RRi from 5 min static rest conditions, the latter also to measure the relative tidal volume and respiratory rate. The validation comprised the Student’s t-test, Bland-Altman analysis, Intraclass Correlation Coefficient and Lin’s concordance correlation. The possible impact of the respiratory activity on the agreement between ECG and Pneumonitor was also assessed.
RESULTS: Acceptable agreement for number of RRi, mean RR, HR and HRV measures calculated based on RRi acquired using ECG and Pneumonitor was presented. There was no association between breathing pattern and RRi agreement between devices.
CONCLUSIONS: Pneumonitor might be considered appropriate for cardiorespiratory studies in the group of pediatric cardiac patients in rest condition.
Abstract
BACKGROUND: Breathing pattern alterations change the variability and the spectral content of the RR intervals (RRi) from electrocardiogram (ECG). However, actually there is no solution on how to record and control participant’s breathing without influencing its natural rate and depth in heart rate variability (HRV) studies.
AIM: The aim of the study was to assess the validity of the Pneumonitor for acquisition of short-term (5 min) RRi in comparison to the reference ECG method for analysis of heart rate (HR) and HRV parameters in the group of pediatric patients with cardiac disease.
METHODS: Nineteen patients of both sexes participated in the study. ECG and Pneumonitor were used to record RRi from 5 min static rest conditions, the latter also to measure the relative tidal volume and respiratory rate. The validation comprised the Student’s t-test, Bland-Altman analysis, Intraclass Correlation Coefficient and Lin’s concordance correlation. The possible impact of the respiratory activity on the agreement between ECG and Pneumonitor was also assessed.
RESULTS: Acceptable agreement for number of RRi, mean RR, HR and HRV measures calculated based on RRi acquired using ECG and Pneumonitor was presented. There was no association between breathing pattern and RRi agreement between devices.
CONCLUSIONS: Pneumonitor might be considered appropriate for cardiorespiratory studies in the group of pediatric cardiac patients in rest condition.
Keywords
heart rate variability, impedance pneumography, Pneumonitor, RR intervals, validation


Title
Validity of the Pneumonitor for RR intervals acquisition for short-term heart rate variability analysis extended with respiratory data in pediatric cardiac patients
Journal
Kardiologia Polska (Polish Heart Journal)
Issue
Article type
Original article
Published online
2023-03-16
Page views
23
Article views/downloads
10
DOI
10.33963/KP.a2023.0070
Pubmed
Keywords
heart rate variability
impedance pneumography
Pneumonitor
RR intervals
validation
Authors
Jakub S Gąsior
Marcel Młyńczak
Maciej Rosoł
Piotr Wieniawski
Iwona Walecka
Gerard Cybulski
Bożena Werner


- 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. Circulation. 1996; 93(5): 1043–1065.
- Malik M, Hnatkova K, Huikuri HV, et al. CrossTalk proposal: Heart rate variability is a valid measure of cardiac autonomic responsiveness. J Physiol. 2019; 597(10): 2595–2598.
- Gąsior JS, Sacha J, Jeleń PJ, et al. Heart Rate and Respiratory Rate Influence on Heart Rate Variability Repeatability: Effects of the Correction for the Prevailing Heart Rate. Front Physiol. 2016; 7: 356.
- Quintana DS, Heathers JAJ. Considerations in the assessment of heart rate variability in biobehavioral research. Front Psychol. 2014; 5: 805.
- Grossman P, Taylor EW. Toward understanding respiratory sinus arrhythmia: relations to cardiac vagal tone, evolution and biobehavioral functions. Biol Psychol. 2007; 74(2): 263–285.
- Hayano J, Yuda E. Pitfalls of assessment of autonomic function by heart rate variability. J Physiol Anthropol. 2019; 38(1): 3.
- Beda A, Simpson DM, Carvalho NC, et al. Low-frequency heart rate variability is related to the breath-to-breath variability in the respiratory pattern. Psychophysiology. 2014; 51(2): 197–205.
- Soer R, Six Dijkstra MW, Bieleman HJ, et al. Influence of respiration frequency on heart rate variability parameters: A randomized cross-sectional study. J Back Musculoskelet Rehabil. 2021; 34(6): 1063–1068.
- Martín-Montero A, Gutiérrez-Tobal GC, Kheirandish-Gozal L, et al. Heart rate variability spectrum characteristics in children with sleep apnea. Pediatr Res. 2021; 89(7): 1771–1779.
- Gąsior JS, Sacha J, Pawłowski M, et al. Normative Values for Heart Rate Variability Parameters in School-Aged Children: Simple Approach Considering Differences in Average Heart Rate. Front Physiol. 2018; 9: 1495.
- Plaza-Florido A, Sacha J, Alcantara J. Short-term heart rate variability in resting conditions: methodological considerations. Kardiol Pol. 2021; 79(7-8): 745–755.
- Cysarz D, Zerm R, Bettermann H, et al. Comparison of respiratory rates derived from heart rate variability, ECG amplitude, and nasal/oral airflow. Ann Biomed Eng. 2008; 36(12): 2085–2094.
- Smulyan H. The Computerized ECG: Friend and Foe. Am J Med. 2019; 132(2): 153–160.
- Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol. 2021; 18(8): 581–599.
- Sana F, Isselbacher EM, Singh JP, et al. Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review. J Am Coll Cardiol. 2020; 75(13): 1582–1592.
- Tandon A, de Ferranti SD. Wearable Biosensors in Pediatric Cardiovascular Disease. Circulation. 2019; 140(5): 350–352.
- , et al Młyńczak M, Niewiadomski W, Zylinski M, et al. Ambulatory devices measuring cardiorespiratory activity with motion. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), Porto, Portugal, 21-23 February 2017; 91-97.
- Seppä VP, Hyttinen J, Uitto M, et al. Novel electrode configuration for highly linear impedance pneumography. Biomed Tech (Berl). 2013; 58(1): 35–38.
- Młyńczak M, Niewiadomski W, Żyliński M, et al. Assessment of calibration methods on impedance pneumography accuracy. Biomed Tech (Berl). 2016; 61(6): 587–593.
- Młyńczak M, Krysztofiak H. Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof. Front Physiol. 2019; 10: 45.
- Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1(8476): 307–310.
- Zaki R, Bulgiba A, Ismail R, et al. Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review. PLoS One. 2012; 7(5): e37908.
- Pevnick JM, Birkeland K, Zimmer R, et al. Wearable technology for cardiology: An update and framework for the future. Trends Cardiovasc Med. 2018; 28(2): 144–150.
- Milagro J, Gracia-Tabuenca J, Seppa VP, et al. Noninvasive Cardiorespiratory Signals Analysis for Asthma Evolution Monitoring in Preschool Children. IEEE Trans Biomed Eng. 2020; 67(7): 1863–1871.
- Kalidas V, Tamil L. Real-time QRS detector using stationary wavelet transform for automated ECG analysis. 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering. 2017: 457–461.
- Młyńczak M, Cybulski G. Decomposition of the Cardiac and Respiratory Components from Impedance Pneumography Signals. Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies. 2017(4): 26–33.
- Giles DA, Draper N. Heart Rate Variability During Exercise: A Comparison of Artefact Correction Methods. J Strength Cond Res. 2018; 32(3): 726–735.
- Cilhoroz B, Giles D, Zaleski A, et al. Validation of the Polar V800 heart rate monitor and comparison of artifact correction methods among adults with hypertension. PLoS One. 2020; 15(10): e0240220.
- Seely AJE, Macklem PT. Complex systems and the technology of variability analysis. Crit Care. 2004; 8(6): R367–R384.
- Tarvainen MP, Niskanen JP, Lipponen JA, et al. Kubios HRV: heart rate variability analysis software. Comput Methods Programs Biomed. 2014; 113(1): 210–220.
- Tarvainen MP, Ranta-Aho PO, Karjalainen PA. An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng. 2002; 49(2): 172–175.
- Phillips P, Perron P. Testing for a unit root in time series regression. Biometrika. 1988; 75(2): 335–346.
- Abu-Arafeh A, Jordan H, Drummond G. Reporting of method comparison studies: a review of advice, an assessment of current practice, and specific suggestions for future reports. Br J Anaesth. 2016; 117(5): 569–575.
- Hopkins WG, Marshall SW, Batterham AM, et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009; 41(1): 3–13.
- Lee J, Koh D, Ong CN. Statistical evaluation of agreement between two methods for measuring a quantitative variable. Comput Biol Med. 1989; 19(1): 61–70.
- Lin LK. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989; 45(1): 255.
- Gamelin FX, Baquet G, Berthoin S, et al. Validity of the polar S810 to measure R-R intervals in children. Int J Sports Med. 2008; 29(2): 134–138.
- Speer KE, Semple S, Naumovski N, et al. Measuring Heart Rate Variability Using Commercially Available Devices in Healthy Children: A Validity and Reliability Study. Eur J Investig Health Psychol Educ. 2020; 10(1): 390–404.
- Charlton PH, Bonnici T, Tarassenko L, et al. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiol Meas. 2016; 37(4): 610–626.
- Kay JD, Colan SD, Graham TP. Congestive heart failure in pediatric patients. Am Heart J. 2001; 142(5): 923–928.
- Smyth RL. Lessons from normal heart and respiratory rates in children. Lancet. 2011; 377(9770): 974–975.
- Muntaner-Mas A, Martinez-Nicolas A, Lavie CJ, et al. A Systematic Review of Fitness Apps and Their Potential Clinical and Sports Utility for Objective and Remote Assessment of Cardiorespiratory Fitness. Sports Med. 2019; 49(4): 587–600.
- Acampa M, Voss A, Bojić T. Editorial: Cardiorespiratory Coupling-Novel Insights for Integrative Biomedicine. Front Neurosci. 2021; 15: 671900.
- Rosoł M, Młyńczak M, Cybulski G. Granger causality test with nonlinear neural-network-based methods: Python package and simulation study. Comput Methods Programs Biomed. 2022; 216: 106669.
- Młyńczak M, Krysztofiak H. Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes. Front Physiol. 2018; 9: 1455.
- Nuzzi D, Stramaglia S, Javorka M, et al. Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability. Philos Trans A Math Phys Eng Sci. 2021; 379(2212): 20200263.
- Zhu Y, Hsieh YH, Dhingra RR, et al. Quantifying interactions between real oscillators with information theory and phase models: application to cardiorespiratory coupling. Phys Rev E Stat Nonlin Soft Matter Phys. 2013; 87(2): 022709.
- Tzeng YC, Larsen PD, Galletly DC. Cardioventilatory coupling in resting human subjects. Exp Physiol. 2003; 88(6): 775–782.
- Porta A, Castiglioni P, Di Rienzo M, et al. Cardiovascular control and time domain Granger causality: insights from selective autonomic blockade. Philos Trans A Math Phys Eng Sci. 2013; 371(1997): 20120161.
- Joshi R, Kommers D, Long Xi, et al. Cardiorespiratory coupling in preterm infants. J Appl Physiol (1985). 2019; 126(1): 202–213.