Thousands of papers involved in heart rate variability (HRV). However, little was known about one important measure of HRV, the root mean square of successive heartbeat interval differences (RMSSDs). Another fundamental measure SDNN indicates standard deviation of normal to normal R-R intervals, where R is the peak of a QRS complex (heartbeat). Compared with SDNN, RMSSD is a short-term variation of heart rate. Through a time-frequency transformation, the ratio of low- and high-frequency power LF/HF represents the sympatho-vagal balance of the autonomic nervous system (ANS). Some research claimed that SDNN/RMSSD was a good surrogate for LF/HF. However, only two special cases supported this hypothesis in the literature survey. The first happened in resting supine state and the other was a group of prefrontal cortex patients. Both of their Pearson correlation coefficients reached 0.90, a reasonable criterion. In our study, a 6-week experiment was performed with 32 healthy young Asian males. The Pearson correlation coefficients had a normal distribution with average values smaller than 0.6 for 3 and 5-minute epochs, respectively. Our findings suggest this surrogate aspect could remain as a hypothesis. 1. Introduction RMSSD, the root mean square differences of successive R-R (heartbeat) intervals, is a significant indicator for both atrial fibrillation (AF) and sudden unexplained death in epilepsy (SUDEP) [1, 2]. Beyond RMSSD, some other essential variables of heart rate variability (HRV) measures are SDNN, LF and HF. SDNN is the standard deviation of normal to normal R-R intervals. LF and HF represent power in low- and high-frequency ranges [3]. Previous research suggested that SDNN/RMSSD was a good surrogate of LF/HF for healthy subjects [4, 5]. Whether this statement is affirmative would be revised by analysis of cardiac measurements in this paper. Measurements of HRV include time domain, frequency domain methods, and so on. They are noninvasive, as the tools to recognize the relationship between the autonomic nervous system (ANS) and cardiovascular mortality [3]. Figure 1 shows a standard routine of electrocardiogram (ECG) signal processing [6]. Detection of heartbeats (QRS complexes) is the first step, where R is the peak of the complex. The time domain analysis (SDNN, RMSSD) reports the activity of the cardiac system. The frequency domain analysis (LF, HF) reflects sympathovagal balance of the ANS. These HRV variables can be calculated easily through a superlative software package [7]. As time went by, HRV yielded rich fruits in various applications
References
[1]
S. Dash, K. H. Chon, S. Lu, and E. A. Raeder, “Automatic real time detection of atrial fibrillation,” Annals of Biomedical Engineering, vol. 37, no. 9, pp. 1701–1709, 2009.
[2]
C. M. DeGiorgio, P. Miller, S. Meymandi et al., “RMSSD, a measure of vagus-mediated heart rate variability, is associated with risk factors for SUDEP: the SUDEP-7 Inventory,” Epilepsy and Behavior, vol. 19, no. 1, pp. 78–81, 2010.
[3]
A. J. Camm, M. Malik, et al., “Heart rate variability: standards of measurement, physiological interpretation, and clinical use,” Circulation, vol. 93, no. 5, pp. 1043–1065, 1996.
[4]
R. Balocchi, F. Cantini, M. Varanini, G. Raimondi, J. M. Legramante, and A. Macerata, “Revisiting the potential of time-domain indexes in short-term HRV analysis,” Biomedizinische Technik, vol. 51, no. 4, pp. 190–193, 2006.
[5]
J. J. Sollers, T. W. Buchanan, S. M. Mowrer, L. K. Hill, and J. F. Thayer, “Comparison of the ratio of the standard deviation of the r-r interval and the root mean squared successive differences (SD/rMSSD) to the low frequency-to-high frequency (LF/HF) ratio in a patient population and normal healthy controls,” Biomedical Sciences Instrumentation, vol. 43, pp. 158–163, 2007.
[6]
H. M. Wang, Y. L. Lai, M. C. Hou et al., “A ±6？ms-accuracy, 0.68？mm2 and 2.21？μW QRS detection ASIC,” in Proceedings of IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems (ISCAS '10), pp. 1372–1375, June 2010.
[7]
J. P. Niskanen, M. P. Tarvainen, P. O. Ranta-Aho, and P. A. Karjalainen, “Software for advanced HRV analysis,” Computer Methods and Programs in Biomedicine, vol. 76, no. 1, pp. 73–81, 2004.
[8]
U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability: a review,” Medical and Biological Engineering and Computing, vol. 44, no. 12, pp. 1031–1051, 2006.
[9]
H. Otzenberger, C. Gronfier, C. Simon et al., “Dynamic heart rate variability: A tool for exploring sympathovagal balance continuously during sleep in men,” American Journal of Physiology, vol. 275, no. 3, pp. H946–H950, 1998.
[10]
J. Penttil？, A. Helminen, T. Jartti et al., “Time domain, geometrical and frequency domain analysis of cardiac vagal outflow: effects of various respiratory patterns,” Clinical Physiology, vol. 21, no. 3, pp. 365–376, 2001.
[11]
G. G. Berntson, D. L. Lozano, and Y. J. Chen, “Filter properties of root mean square successive difference (RMSSD) for heart rate,” Psychophysiology, vol. 42, no. 2, pp. 246–252, 2005.
[12]
E. Gil, M. Orini, R. Bailón, J. M. Vergara, L. Mainardi, and P. Laguna, “Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions,” Physiological Measurement, vol. 31, no. 9, pp. 1271–1290, 2010.
[13]
C. Orwat, A. Graefe, and T. Faulwasser, “Towards pervasive computing in health care—a literature review,” BMC Medical Informatics and Decision Making, vol. 8, article no. 26, 2008.
[14]
L. Brown, B. Grundlehner, J. Van De Molengraft, J. Penders, and B. Gyselinckx, “Body area network for monitoring autonomic nervous system responses,” in Proceedings of 3rd International Conference on Pervasive Computing Technologies for Healthcare—Pervasive Health 2009 (PCTHealth '09), pp. 1–3, April 2009.
[15]
S. Borromeo, C. Rodriguez-Sanchez, F. Machado, J. A. Hernandez-Tamames, and R. De La Prieta, “A reconfigurable, wearable, wireless ECG system,” in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society (EMBC '07), pp. 1659–1662, August 2007.
[16]
C. W. Lin, J. S. Wang, and P. C. Chung, “Mining physiological conditions from heart rate variability analysis,” IEEE Computational Intelligence Magazine, vol. 5, no. 1, pp. 50–58, 2010.
[17]
H. Holm, D. F. Gudbjartsson, D. O. Arnar et al., “Several common variants modulate heart rate, PR interval and QRS duration,” Nature Genetics, vol. 42, no. 2, pp. 117–122, 2010.
[18]
R. Auer, et al., “Association of major and minor ECG abnormalities with coronary heart disease events,” Journal of the American Medical Association, vol. 307, no. 14, pp. 1497–1505, 2012.
[19]
J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230–236, 1985.
[20]
J. Mateo and P. Laguna, “Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 334–343, 2003.
[21]
S. McKinley and M. Levine, “Cubic spline interpolation,” http://online.redwoods.cc.ca.us/instruct/darnold/laproj/Fall98/SkyMeg/proj.pdf.
[22]
M. P. Tarvainen, P. O. Ranta-aho, and P. A. Karjalainen, “An advanced detrending method with application to HRV analysis,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 2, pp. 172–175, 2002.
[23]
P. Welch, “The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70–73, 1967.
[24]
S. M. Kay and S. L. Marple, “Spectrum analysis: a modern perspective,” Proceedings of the IEEE, vol. 69, no. 11, pp. 1380–1419, 1981.
[25]
B. Pomeranz, J. B. Macaulay, and M. A. Caudill, “Assessment of autonomic functions in humans by heart rate spectral analysis,” American Journal of Physiology, vol. 17, no. 1, pp. H151–H153, 1985.
[26]
M. Pagani, F. Lombardi, and S. Guzzetti, “Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog,” Circulation Research, vol. 59, no. 2, pp. 178–193, 1986.
[27]
A. J. Hayter, Probability and Statistics For Engineers and Scientists, Thomson Higher Education, Belmont, California, USA, 2007.
[28]
R. E. Walpole, et al., Probability & Statistics For Engineers and Scientists, Pearson Education, Boston, Mass, USA, 2012.
[29]
R. J. Larsen and M. L. Marx, An Introduction To Mathematical Statistics and Its Application, Pearson Education, Boston, Mass, USA, 2012.
[30]
T. Thong, et al., “Accuracy of ultra-short heart rate variability measures,” in Proceedings of the 25th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, vol. 3, pp. 2424–2427, 2003.
[31]
J. McNames, T. Thong, and B. Goldstein, “Reliability and accuracy of heart rate variability metrics versus ECG segment duration,” in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 212–215, September 2003.
[32]
J. McNames and M. Aboy, “Reliability and accuracy of heart rate variability metrics versus ECG segment duration,” Medical and Biological Engineering and Computing, vol. 44, no. 9, pp. 747–756, 2006.
[33]
K. B. Min, J. Y. Min, D. Paek, S. I. Cho, and M. Son, “Is 5-minute heart rate variability a useful measure for monitoring the autonomic nervous system of workers?” International Heart Journal, vol. 49, no. 2, pp. 175–181, 2008.
[34]
E. B. Schroeder, E. A. Whitsel, G. W. Evans, R. J. Prineas, L. E. Chambless, and G. Heiss, “Repeatability of heart rate variability measures,” Journal of Electrocardiology, vol. 37, no. 3, pp. 163–172, 2004.
[35]
G. D. Pinna, R. Maestri, A. Torunski et al., “Heart rate variability measures: a fresh look at reliability,” Clinical Science, vol. 113, no. 3-4, pp. 131–140, 2007.
[36]
G. R. H. Sandercock, P. D. Bromley, and D. A. Brodie, “The reliability of short-term measurements of heart rate variability,” International Journal of Cardiology, vol. 103, no. 3, pp. 238–247, 2005.
[37]
T. H. Tarkiainen, K. L. Timonen, P. Tiittanen et al., “Stability over time of short-term heart rate variability,” Clinical Autonomic Research, vol. 15, no. 6, pp. 394–399, 2005.
[38]
M. V. Pitzalis, F. Mastropasqua, F. Massari et al., “Short- and long-term reproducibility of time and frequency domain heart rate variability measurements in normal subjects,” Cardiovascular Research, vol. 32, no. 2, pp. 226–233, 1996.
[39]
M. A. Kowalewski and M. Urban, “Short- and long-term reproducibility of autonomic measures in supine and standing positions,” Clinical Science, vol. 106, no. 1, pp. 61–66, 2004.
