Ultra-short-term heart rate variability analysis: comparison between Poincaré and frequency domain methods
Keywords:
LF/HF, SD21, short-term heart rate variability, ultra-short-term heart rate variabilityAbstract
Short-term (5 min) heart rate variability analysis (HRV) demand for monitoring people's health and well-being status is increasing due to portable sensors and mobile applications. Five minutes analysis is extensive compared to that used in measurements of glucose, blood pressure, temperature, etc. To reduce costs and achieve optimal performance in personal health assessment, ultra-short-term monitoring less than 5 min is suggested. The aim of this work is to establish the minimum ultra-short-term HRV analysis time required for LF/HF (LH) (frequency domain sympathy-vagal balance indicator) and SD21 (Poincaré graphic domain sympathy-vagal balance indicator) to be equivalent to those obtained by 5 min HRV. From electrocardiograms (ECGs), processing of 23 subjects in the experiment and series of 60, 90, 120, 180, 240 and 300 s (5 min) were obtained. The HRV time series was calculated and each LH and SD21 indicator in each of the ultra-short series was compared with the 300 s (Gold Standard), using different concordance analysis methods, such us Pearson correlation, Bland Altman, Cohen Delta. The ultra-short-term HRV LH indicator is equivalent to short-term HRV (5 min) in series equal or greater than 180 s while SD21 was equivalent to series equal or greater than 120 s.
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M. Risk, J. Sobh, et al., ”Variabilidad de las señales cardiorespiratorias. Parte 2: Variabilidad a largo plazo.” Revista Argentina de Bioingenierı́a, vol.2, no.2, 1996.
M. Risk, J. Sobhet et al., ”Variabilidad de las señales cardiorespiratorias. Parte 1: Variabilidad a corto plazo.” Revista Argentina de Bioingenierı́a, vol.2, no.1, 1996.
M. Malik, “Heart rate variability guidelines: standards of measurement, physiological interpretation, and clinical use.” European Heart Journal, vol. 17, pp. 354–381, 1996.
L. Gamero, M. Risk, et al., ”Heart rate variability analysis using wavelet transform.” Computers in Cardiology, Indianapolis, IN, USA, 1996, pp. 177-180, doi: 10.1109/CIC.1996.542502.
F. Shaffer, S. Shearman and Z. Meehan, “The Promise of Ultra-Short-Term (UST) Heart Rate Variability Measurements.” Biofeedback, vol. 44, no. 4, pp. 229-233, 2016.
S. Massaro, L. Pecchia, “Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience.” Organizational Research Methods, vol. 22, no. 12, Dicember 2016.
M. Risk, V. Bril, et al., ”Heart rate variability measurement in diabetic neuropathy: review of methods.” Diabetes technology & therapeutics, vol. 3 , no. 1, pp. 6–76, 2001.
I. Bonyhay, M. Risk and R. Freeman, ”High-pass filter characteristics of the baroreflex a comparison of frequency domain and pharmacological methods.” PloS one, vol., no, pp. 8–11, e79513. doi:10.1371/journal.pone.00795132013, 2013.
R. Buendia, F. Forcolin, J. Karlsson et al.,”Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness.” Traffic Injury Prevention, vol. 20, no. 3, pp. 249–254, 2019.
U. Bracale, M. Rovani, M. Bracale, et al., “Totally laparoscopic gastrectomy for gastric cancer: meta-analysis of short-term outcomes.” Minimally Invasive Therapy Allied Technol, vol. 21, no. 3, pp. 150-160, May 2012.
P. Melillo, A. Jovic, N. De Luca, et al., “Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.” Healthc. Technol. Lett., vol. 2, no. 4, pp. 89-94, Aug 2015.
Q. Xu, T. L. Nwe, and C. Guan, “Cluster-based analysis for personalized stress evaluation using physiological signals,” in IEEE J. Biomed. Health Inform., vol. 19, no. 1, pp. 275-281, Jan 2015.
J. Gallardo, G. Bellone, S. Plano, D. Vigo and M. Risk, “Heart Rate Variability: Influence of Pre-processing Methods in Identifying Single-Night Sleep-Deprived Subjects.” J. Med. Biol. Eng., https://doi.org/10.1007/s40846-020-00595-8, Jan 2021.
S. Mayya, V. Jilla, et al, ”Continuous monitoring of stress on smartphone using heart rate variability,” in 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), Belgrade, 2015, pp. 1-5. doi: 10.1109/BIBE.2015.7367627.
J. Vicente, P. Laguna et al., ”Drowsiness detection using heart rate variability.” Medical & Biological Engineering & Computing, vol. 54 no. 6, pp. 927-937, 2016.
L. Pecchia, R. Castaldo, L. Montesinos and P. Melillo, “Are ultra-short heart rate variability features good surrogates of short-term ones? State of the art review and recommendations.” Healthcare Technology Letters, vol. 5, no. 3, pp. 94-100, Jun 2018.
L. Sathyapriya, L. Murali, and T. Manigandan, “Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm,” in Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014, (978), pp. 483-487.
J. McNames, T. Thong, and M. Aboy, “Impulse rejection filter for artifact removal in spectral analysis of biomedical signals,” in Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, 2004,. CA, USA, vol. 1, pp. 145-8.
R. A. Thuraisingham, “Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals.” Computer Methods and Programs in Biomedicine vol. 83, no. 1, pp. 78-82, July 2006.
D. Wejer, D. Makowiec, Z. R. Struzik, and M. Żarczyńska-Buchowiecka, “Impact of the Editing of Patterns with Abnormal RR intervals on the Assessment of Heart Rate Variability.” Acta Physica Polonica B, vol. 45, no. 11, pp. 2103, 2014.
A. E. Aubert, D. Ramaekers, and F. Beckers, “Analysis of heart rate variability in unrestrained rats. Assessment of method and results.“ Med. Biol. Eng. Comp., vol. 60, no. 3, pp. 197-213, Nov. 1999.
M. A. Peltola, “Role of editing of R-R intervals in the analysis of heart rate variability.” Frontiers in Physiology, vol.3, pp. 148, May 2012.
P. Laguna, G. B. Moody, R. G. Mark, ”Power spectral density of unevenly sampled data by least-square analysis: Performance and ap-
plication to heart rate signals.” IEEE Trans. Biomed. Eng., vol. 45, no. 6, pp. 698–715, 1998.
B. Saini, D. Singh, et al., ”Improved Power Spectrum Estimation for RR-Interval Time Series.” Int. J. Electr. Comput. Energ. Electron. Commun.
Eng., vol. 2, no. 10, pp. 154–158, 2008.
J. Camm, ”Guidelines Heart rate variability.” Eur. Heart J., vol. 17, pp. 354–381, 1996.
M. Brennan, M. Palaniswami and P. Kamen, ”Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?,” in IEEE Transactions on Biomedical Engineering, vol. 48, no.11, pp. 1342-1347, Nov. 2001. doi: 10.1109/10.959330
P. K. Stein, P. P. Domitrovich, et al., ”Traditional and nonlinear heart rate variability are each independently associated with mortality after myocardial infarction.” J. Cardiovasc. Electrophysiol, vol. 16, no. 1, pp. 13–20, 2005.
P. Guzik, J. Piskorski, et al., ”Correlations between the Poincaré Plot and Conventional Heart Rate Variability Parameters Assessed during Paced Breathing.” J. Physiol. Sci., vol. 57, no. 1, pp. 63–71, 2007.
C. H. Hsu, M. Y. Tsai, et al., ”Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction.” Acta Anaesthesiol. Taiwanica, vol. 50, no 1, pp. 12–18, 2012.
R. A. Hoshi, C. M. Pastre et al., ”Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables.” Auton. Neurosci. Basic Clin., vol. 177, no. 2, pp. 271–274, 2013.
J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement.” The Lancet, vol. 1, pp. 307-310, Feb. 1986.
J. M. Bland and D. G. Altman, “Measuring agreement in method comparison studies.” Statistical Methods in Medical Research, vol. 8, pp. 135-160, Jun. 1999.
J. Cohen, Statistical power analysis for the behavioral sciences. United States of America: Lawrence Earlbaum Associates. Hillsdale, NJ, 1988.
M. Muñoz, A. Van Roon, H. Riese et al., “Validity of (Ultra) Short recordings for heart rate variability measurements.” PLoS ONE, vol. 10, no. 9, pp. 1-15, Sep. 2015.
M. Nardelli, A. Greco, J. Bolea et al., “Reliability of Lagged Poincaré Plot parameters in ultra-short Heart Rate Variability series: Application on Affective Sounds,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 741-749, May 2018.
F. Shaffer and J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms.” Frontiers in Public Health, vol. 5, pp. 1-17, September 2017.
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