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41 - Real-time activity energy expenditure estimation for embedded ambulatorysystems using Sensium™ technologies

from Part VIII - Future perspectives

Published online by Cambridge University Press:  05 September 2015

By
Su-Shin Ang ,
Ksawery Wieczorkowski-Rettinger  and
Miguel Hernandez-Silveira
Edited by
Sandro Carrara  and
Krzysztof Iniewski
Su-Shin Ang
Affiliation:
Sensium Healthcare
Ksawery Wieczorkowski-Rettinger
Affiliation:
Sensium Healthcare
Miguel Hernandez-Silveira
Affiliation:
Toumaz Healthcare Ltd.
Sandro Carrara
Affiliation:
École Polytechnique Fédérale de Lausanne
Krzysztof Iniewski
Affiliation:
Redlen Technologies Inc., Canada
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Summary

Introduction

Lack of physical activity (PA) and exercise is a widespread and prevalentproblem in the modern society. A study conducted by the US Department ofHealth in 2002 showed that the lack of PA is associated with a wide range ofconditions including obesity, diabetes, heart disease, stroke, andosteoporosis [1]. In 2008, the World Health Organization reported that 11%of the world’s population (at 25+ years of age) was estimated to besuffering from diabetes [2], and diabetes care alone may account for up to15% of national healthcare budgets [2]. These statistics reflects thenegative economic impact to healthcare systems worldwide.

Physical activity intensity (PAI) and energy expenditure (PAEE) can beestimated from measurements of oxygen consumption using portable gasanalyser systems. However, these indirect calorimeters are complex, bulky,heavy, obtrusive, and very expensive, and hence unsuitable for routine use.On the other hand, there is a large body of evidence (discussed later inthis chapter) suggesting that PA activity intensity and energy expenditurecan be estimated and continuously monitored using physiologicaland/or biomechanical information, owing to the relationship of theseparameters with oxygen consumption during aerobic exercise [3–5]. Inaddition, recent advances in microelectronics have enabled the developmentof miniaturized integrated circuits, medical sensors and micro-engineeredinertial sensors.

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Handbook of Bioelectronics
Directly Interfacing Electronics and Biological Systems
 , pp. 513 - 542
Publisher: Cambridge University Press
Print publication year: 2015

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References

Services, H.a.H., Physical Activity Fundamental to Preventing Disease. 2002. USA: Google Scholar
WHO, Global status report on NCDs 2010. 2010.
Ekelund, U., et al., Physical activity in relation to aerobic fitness and body fat in 14-to 15-year-old boys and girls. European Journal of Applied Physiology, 2001. 85(3): p. 195–201.CrossRefGoogle ScholarPubMed
Rowlands, A.V., Eston, R.G., and Ingledew, D.K., Measurement of physical activity in children with particular reference to the use of heart rate and pedometry. Sports Medicine (Auckland, NZ), 1997. 24(4): p. 258.CrossRefGoogle ScholarPubMed
Corder, K., et al., Assessment of physical activity in youth. Journal of Applied Physiology, 2008. 105(3): p. 977–987.CrossRefGoogle ScholarPubMed
Lymberis, A. and Dittmar, A., Advanced wearable health systems and applications – Research and development efforts in the European Union. Engineering in Medicine and Biology Magazine, IEEE, 2007. 26(3): p. 29–33.CrossRefGoogle Scholar
Lymberis, A. and Paradiso, R.. Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges. in Engineering in Medicine and Biology Society EMBS 2008. 30th Annual International Conference of the IEEE. 2008: IEEE.Google Scholar
Madan, A., Moturu, A. T., Lazer, D. and Pentland, A., Social sensing: obesity, unhealthy eating and exercise in face-to-face networks. in Wireless Health 2010. 2010: ACM.Google Scholar
Wasserman, K., Hansen, J.E.., Sue, D. Y. et al. Principles of Exercise Testing and Interpretation. 2005, Philadelphia, USA: Lippincott Williams & Wilkins.Google Scholar
Davenport, P., Energy expenditure repeatability/analysis of calibration measures of the Actiheart monitor, in Biomedical Engineering. Unpublished PhD thesis, 2010, University of Surrey.
Frankenfield, D. C., Muth, E. R. and Rowe, W. A., The Harris-Benedict studies of human basal metabolism: history and limitations. American Journal of Diet Association, 1998. 98(4): p. 439–445.CrossRefGoogle ScholarPubMed
Westerterp, K. R., Wilson, S. A. and Rolland, V., Diet induced thermogenesis measured over 24h in a respiration chamber: effect of diet compositionInternational Journal of Obesity, 1999. 23(3): p. 287–292.CrossRefGoogle Scholar
Astrup, A., Gotzsche, P. C. and Werken, K. V. D., Meta-analysis of resting metabolic rate in formerly obese subjects. American Journal of Clinical Nutrition, 1999. 69(6): p. 1117–1122.CrossRefGoogle ScholarPubMed
Waters, R. L. and Mulroy, S., The energy expenditure of normal and pathologic gait. Journal of Gait Posture, 1999. 9(3): p. 207–231.CrossRefGoogle ScholarPubMed
Ainsworth, B. E., Haskell, W. L., Whitt, M. C. et al. Compendium of physical activities: an update of activity codes and MET intensities. Journal of Medicine and Science in Sports and Exercise, 2000. 32(9): p. 498–504.CrossRefGoogle ScholarPubMed
Hood, V. L., Grant, M. H., Maxwell, D. J. and Hasler, J. P., A new method of using heart rate to represent energy expenditure: The total heart beat index. Archives of Physical Medicine and Rehabilitation, 2002. 83(9): p. 1266–1273.CrossRefGoogle ScholarPubMed
Plasgui, G. and Westrup, K. R., Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Journal of Obesity, 2007. 15(10): p. 2371–2379.CrossRefGoogle Scholar
Weir, J., New methods for calculating metabolic rate with special reference to protein metabolism. Journal of Physiology, 1949. 109: p. 1–9.CrossRefGoogle ScholarPubMed
Brage, S., Brage, N., Franks, P. W. et al. Branched equation model of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. Journal of Applied Physiology, 2004. 96: p. 343–351.CrossRefGoogle ScholarPubMed
Johansson, H. P., Rossander-Hulthen, L., Slinde, F. and Ekblom, B., Accelerometry combined with heart rate telemetry in the assessment of total energy expenditure. Journal of Nutrition, 2006. 95: p. 631–639.CrossRefGoogle Scholar
Murphy, S.L., Review of physical activity measurement using accelerometers in older adults: considerations for research design and conduct. Journal of Preventive Medicine, 2009. 48(2): p. 108–114.CrossRefGoogle ScholarPubMed
Bussmann, H. B., Reuvecamp, P.J., Martens, W. L. and Stam, H. J., Validity and reliability of measurements obtained with an “activity monitor” in people with and without a transtibial amputation. Journal of Physical Therapy, 1998. 78(9): p. 989–998.CrossRefGoogle ScholarPubMed
Zhang, K., Pi-Sunyer, F. X. and Boozer, C. N., Improving energy expenditure estimation for physical activity. Journal of Medicine and Science in Sports and Exercise, 2004. 36(5): p. 883–889.CrossRefGoogle ScholarPubMed
Bodymedia. [cited 2013 1 March]; Available from: .
Simonson, D.C. and DeFronzo, R.A., Indirect calorimetry: methodological and interpretative problems. American Journal of Physiology – Endocrinology and Metabolism, 1990. 258(3): p. E399-E412.CrossRefGoogle ScholarPubMed
Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability, standards of measurement, physiological interpretation, and clinical use. Journal of Circulation, 1996. 93: p. 1043–1065.CrossRefGoogle Scholar
Khandoker, A.H., Jelinek, H.F., and Palaniswami, M., Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. Biomedical Engineering online, 2009. 8(1): p. 3.CrossRefGoogle ScholarPubMed
Seyd, P.A., Ahamed, V.T., and Jacob, J., Time and frequency domain analysis of heart rate variability and their correlations in diabetes mellitus. International Journal of Biological and Medical Sciences, 2009. Google Scholar
Pagani, M., Heart rate variability and diabetic neuropathy. Journal of Diabetes Nutrition Metabolism, 2000. 13(6): p. 341–346.Google ScholarPubMed
Javorka, M., Javorkova, J., Tonhajzerova, I., Calkovska, A. and Javorka, K., Heart rate variability in young patients with diabetes mellitus and healthy subjects explored by Poincaré and sequence plots. Journal of Clinical Physiology Functional Imaging 2005. 25: p. 119–127.CrossRefGoogle ScholarPubMed
Kleiger, R.E., Miller, J.P., Bigger, J.T. and Moss, A.J., Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. The American Journal of Cardiology, 1987. 59(4): p. 256–262.CrossRefGoogle ScholarPubMed
Bigger, J.T., Fleiss, J.L., Rolnizky, L.M. and Steinman, R.C., Frequency domain measures of heart period variability to assess risk late after myocardial infarction. Journal of the American College of Cardiology, 1993. 21(3): p. 729–736.CrossRefGoogle ScholarPubMed
BiggerJr, J.T., Fleiss, J.L., Steinman, R.C. et al., Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation, 1992. 85(1): p. 164–171.CrossRefGoogle ScholarPubMed
Spaak, J., Tomlinson, G., McGowan, C.L. et al., Dose-related effects of red wine and alcohol on heart rate variability. American Journal of Physiology – Heart and Circulatory Physiology, 2010. 298(6): p. H2226–H2231.CrossRefGoogle ScholarPubMed
Park, S.K., Tucker, K.L., O’Neill, M.S. et al., Fruit, vegetable, and fish consumption and heart rate variability: the Veterans Administration Normative Aging Study. The American Journal of Clinical Nutrition, 2009. 89(3): p. 778–786.CrossRefGoogle ScholarPubMed
Hibino, G., Moritani, T., Kawada, T. et al., Caffeine enhances modulation of parasympathetic nerve activity in humans: quantification using power spectral analysis. The Journal of Nutrition, 1997. 127(7): p. 1422–1427.CrossRefGoogle ScholarPubMed
Lucini, D., et al., Hemodynamic and autonomic adjustments to real life stress conditions in humans. Hypertension, 2002. 39(1): p. 184–188.CrossRefGoogle ScholarPubMed
Tharion, E., Parthasarathy, S. and Neelakantan, N., Short-term heart rate variability measures in students during examinations. National Medical Journal of India, 2009. 22(2): p. 63–66.Google ScholarPubMed
Hjortskov, N., Rissen, D., Blangsted, A.K. et al., The effect of mental stress on heart rate variability and blood pressure during computer work. European Journal of Applied Physiology, 2004. 92(1): p. 84–89.CrossRefGoogle ScholarPubMed
Cervantes Blásquez, J.C., Rodas Font, G. and Capdevila Ortís, L., Heart-rate variability and precompetitive anxiety in swimmers. Psicothema, 2009. 21(4): p. 531–536.Google ScholarPubMed
Kofman, O., Meiron, N., Greenberg, E. et al., Enhanced performance on executive functions associated with examination stress: Evidence from task-switching and Stroop paradigms. Cognition and Emotion, 2006. 20(5): p. 577–595.CrossRefGoogle Scholar
Tulppo, M.P., Makikallio, T.H., Takala, T.E. et al., Quantitative beat-to-beat analysis of heart rate dynamics during exercise. American Journal of Physiology – Heart and Circulatory Physiology, 1996. 271(1): p. H244-H252.CrossRefGoogle ScholarPubMed
Mendonca, G.V., Fernhall, B., Heffernan, K.S. and Pereira, F.D., Spectral methods of heart rate variability analysis during dynamic exercise. Clinical Autonomic Research, 2009. 19(4): p. 237–245.CrossRefGoogle ScholarPubMed
Le Gallais, D., Nonlinear analyses of heart rate variability during heavy exercise and recovery in cyclists. International Journal of Sports Medicine, 2005. 26: p. 1–6.Google Scholar
Baselli, G., Biancardi, L., Perini, R. et al. Heart rate variability during dynamic exercise in supine position in sedentary subjects. in Proceedings Computers in Cardiology 1991. 1991: IEEE: p. 437–440.Google Scholar
Hautala, A.J., Karjalainen, J., Kiviniemi, A.M. et al., Physical activity and heart rate variability measured simultaneously during waking hours. American Journal of Physiology – Heart and Circulatory Physiology, 2010. 298(3): p. H874–H880.CrossRefGoogle ScholarPubMed
Dewey, F.E., Freeman, J.V., Engel, G. et al., Novel predictor of prognosis from exercise stress testing: heart rate variability response to the exercise treadmill test. American Heart Journal, 2007. 153(2): p. 281.CrossRefGoogle ScholarPubMed
Robinson, B.F., Epstein, S.E., Beiser, G.D. and Braunwald, E., Control of heart rate by the autonomic nervous system studies in man on the interrelation between baroreceptor mechanisms and exercise. Circulation Research, 1966. 19(2): p. 400–411.CrossRefGoogle ScholarPubMed
Orizio, C., Perini, R., Comande, A. et al., Plasma catecholamines and heart rate at the beginning of muscular exercise in man. European Journal of Applied Physiology and Occupational Physiology, 1988. 57(5): p. 644–651.CrossRefGoogle ScholarPubMed
Cottin, F., Medigue, C., Lopes, P. et al., Effect of exercise intensity and repetition on heart rate variability during training in elite trotting horse. International Journal of Sports Medicine, 2005. 26(10): p. 859–867.CrossRefGoogle ScholarPubMed
Mateo, J., Serrano, P., Bailon, R. et al. Heart rate variability measurements during exercise test may improve the diagnosis of ischemic heart disease. in Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE. 2001: IEEE.
Roach, D., Wilson, W., Ritchie, D. et al., Dissection of long-range heart rate variability: controlled induction of prognostic measures by activity in the laboratory. Journal of the American College of Cardiology, 2004. 43(12): p. 2271–2277.CrossRefGoogle ScholarPubMed
Bernardi, L., Valle, F., Coco, M., Calciati, A. and Sleight, P., Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. Cardiovascular Research, 1996. 32(2): p. 234–237.CrossRefGoogle ScholarPubMed
Tulppo, M.P., Mäkikallio, T.H., Seppänen, T. et al., Vagal modulation of heart rate during exercise: effects of age and physical fitness. American Journal of Physiology – Heart and Circulatory Physiology, 1998. 274(2): p. H424–H429.CrossRefGoogle ScholarPubMed
Cottin, F., Médigue, C., Leprêtre, P.M. et al., Heart rate variability during exercise performed below and above ventilatory threshold. Medicine and Science in Sports and Exercise, 2004. 36(4): p. 594–600.CrossRefGoogle ScholarPubMed
Van De Water, J.M., Mount, B.E., Barela, J.R. et al., Monitoring the chest with impedance. Chest, 1973. 64(5): p. 597–603.CrossRefGoogle ScholarPubMed
Yasuma, F. and Hayano, J.-I., Respiratory sinus arrhythmia. Why does the heartbeat synchronize with respiratory rhythm?Chest Journal, 2004. 125(2): p. 683–690.CrossRefGoogle ScholarPubMed
Berntson, G.G., Cacioppo, J.T. and Quigley, K.S., Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology, 1993. 30(2): 183–196.CrossRefGoogle ScholarPubMed
Moody, G.B., Mark, R.G., Zoccola, A. and Mantero, S., Derivation of respiratory signals from multi-lead ECGs. Computers in Cardiology, 1985. 12: p. 113–116.Google Scholar
Khan, A.M., Human activity recognition using a single tri-axial accelerometer. Unpublished PhD thesis. 2011, Kyung Hee University, Seoul, Korea.
Preece, S.J., et al., A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. Biomedical Engineering, IEEE Transactions on, 2009. 56(3): p. 871–879.CrossRefGoogle ScholarPubMed
Zhang, K., Pi-Sunyer, F.X., and Boozer, C.N., Improving energy expenditure estimation for physical activity. Medicine and Science in Sports and Exercise, 2004. 36(5): p. 883–889.CrossRefGoogle ScholarPubMed
Mathie, M.J., Coster, A.C., Lovell, N.H., and Celler, B.G., Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 2004. 25(2): p. R1.CrossRefGoogle ScholarPubMed
Pirttikangas, S., Fujinami, K. and Nakajima, T., Feature selection and activity recognition from wearable sensors. Ubiquitous Computing Systems, 2006: p. 516–527.CrossRefGoogle Scholar
Ermes, M., Pärkka, J., Mantyjarvi, J. and Korhonen, I., Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Information Technology in Biomedicine, IEEE Transactions on, 2008. 12(1): p. 20–26.CrossRefGoogle ScholarPubMed
Fahrenberg, J., Foerster, F., Smeja, M. and Müller, W., Assessment of posture and motion by multichannel piezoresistive accelerometer recordings. Psychophysiology, 1997. 34(5): p. 607–612.CrossRefGoogle ScholarPubMed
Fahrenberg, J., Müller, W., Foerster, F. and Smeja, M., A multi-channel investigation of physical activity. Journal of Psychophysiology, 1996. 10: p. 209–217.Google Scholar
Karantonis, D.M., Narayana, M.R., Mathie, M. et al., Implementation of a real-time human movement classifier using a tri-axial accelerometer for ambulatory monitoring. Information Technology in Biomedicine, IEEE Transactions on, 2006. 10(1): p. 156–167.CrossRefGoogle Scholar
Bao, L. and Intille, S., Activity recognition from user-annotated acceleration data. Pervasive Computing, 2004: p. 1–17.Google Scholar
Foerster, F. and Fahrenberg, J., Motion pattern and posture: correctly assessed by calibrated accelerometers. Behavior Research Methods, 2000. 32(3): p. 450–457.CrossRefGoogle ScholarPubMed
Sugimoto, A., Hara, Y., Findley, T.W., and Yoncmoto, K., A useful method for measuring daily physical activity by a three-direction monitor. Scandinavian Journal of Rehabilitation Medicine, 1997. 29(1): p. 37.Google ScholarPubMed
Tamura, T., Sekine, M., Ogawa, M. et al., Classification of acceleration waveforms during walking by wavelet transform. Methods of Information in Medicine, 1997. 36: p. 356–359.Google ScholarPubMed
Nyan, M., Tay, F.E.H., Seah, K.H.W., and Sitoh, Y.Y., Classification of gait patterns in the time–frequency domain. Journal of Biomechanics, 2006. 39(14): p. 2647–2656.CrossRefGoogle ScholarPubMed
Lovell, N., et al. Accelerometry based classification of walking patterns using time-frequency analysis. in Engineering in Medicine and Biology Society EMBS 2007. 29th Annual International Conference of the IEEE. 2007: IEEE.
Mitchell, T.M., Machine Learning. McGraw-Hill International; 1997.Google Scholar
Zhang, M.-L., Peña, J.M. and Robles, V., Feature selection for multi-label naive Bayes classification. Information Sciences, 2009. 179(19): p. 3218–3229.CrossRefGoogle Scholar
Ravi, N., Nikhil, D., Mysore, O. and Littman, M.. Activity recognition from accelerometer data. in Proceedings of the National Conference on Artificial Intelligence. 2005: Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press.
Kwapisz, J.R., Weiss, G.M. and Moore, S.A., Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter, 2011. 12(2): p. 74–82.CrossRefGoogle Scholar
Thompson, D., Batterham, A.M., Bock, S., Robson, C. and Stokes, K., Assessment of low-to-moderate intensity physical activity thermogenesis in young adults using synchronized heart rate and accelerometry with branched-equation modeling. Journal of Nutrition, 2006. 136(4): p. 1037 – 1042.CrossRefGoogle ScholarPubMed
Brage, S., Brage, N., Franks, P.W., Ekelund, U. and Wareham, N.J., Reliability and validity of the combined heart rate and movement sensor Actiheart. European Journal of Clinical Nutrition, 2005. 59(4): p. 561–570.CrossRefGoogle ScholarPubMed
Rettinger, K.W., , S.A., Silveira, M.H. (Toumaz Healthcare Limited), Apparatus and method for estimating energy expenditure. UK Patent office application no.1305387.1, 2013.
Singh, D., Vinod, K., Saxena, S.C. and Deepak, K.K., Effects of RR segment duration on HRV spectrum estimation. Physiological Measurement, 2004. 25(3): p. 721.CrossRefGoogle ScholarPubMed
Antonsson, E.K. and Mann, R.W., The frequency content of gait. Journal of Biomechanics, 1985. 18(1): p. 39–47.CrossRefGoogle Scholar
Kim, K.K., Kim, J.S., Lim, Y.G. and Park, K.S., The effect of missing RR-interval data on heart rate variability analysis in the frequency domain. Physiological Measurement, 2009. 30(10): p. 1039.CrossRefGoogle ScholarPubMed
Saini, B., Singh, D., Uddin, M. and Kumar, V., Improved power spectrum estimation for RR-interval time series. International Electrical, Robotics, Electronics and Communications Engineering 2008. 2(10): p. 154.Google Scholar
Hernandez-Silveira, M., Ang, S-S. and Burdett, A. (Toumaz Healthcare Limited), Respiration monitoring method and system. UK Patent Application Number: 1103008.7, 2011. US Patent 20130331723.
Lomb, N., Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science, 1976. 39(2): p. 447–462.CrossRefGoogle Scholar
Cortes, C. and Vapnik, V., Support-vector networks. Machine Learning, 1995. 20(3): p. 273–297.CrossRefGoogle Scholar
Hernandez-Silveira, M., Mehta, T., Ang, S.S. et al. Implementation and evaluation of a physical activity recognition algorithm in a Sensium body-worn device, in Biodevices Conference. Vilamoura, Portugal, 2012.
Rettinger, K. W., Ang, S-S. and Hernandez-Silveira, M. (Toumaz Healthcare Limited), Apparatus and Method for Estimating Energy Expenditure, UK Patent 1305393.9, 2013.Google Scholar
Scargle, J.D., Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. The Astrophysical Journal, 1982. 263: p. 835–853.CrossRefGoogle Scholar
Shin, K., et al. The direct power spectral estimation of unevenly sampled cardiac event series. in Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE. 1994: IEEE.
Silveira, M. H., Ang, S.-S., Wang, B. and Mehta, T., Implementation and evaluation of a physical activity and energy expenditure algorithm in a Sensium-based body-worn device, in International Conference on Biomedical Electronics and Devices. 2012: Portugal.
Welch, P., The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. Audio and Electroacoustics, IEEE Transactions on, 1967. 15(2): p. 70–73.CrossRefGoogle Scholar
Mcnames, J., Thong, T., and Aboy, M.. Impulse rejection filter for artifact removal in spectral analysis of biomedical signals. in Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th Annual International Conference of the IEEE. 2004: IEEE.
Moody, G.B.Spectral analysis of heart rate without resampling, in Computers in Cardiology 1993, Proceedings. 1993: IEEE.Google Scholar
Rajendra Acharya, U., Joseph, K.P., Kannathal, N. et al., Heart rate variability: a review. Medical and Biological Engineering and Computing, 2006. 44(12): p. 1031–1051.CrossRefGoogle ScholarPubMed
Boardman, A., Schlindwein, F.S., Rocha, A.P. and Leite, A., A study on the optimum order of autoregressive models for heart rate variability. Physiological Measurement, 2002. 23(2): p. 325.CrossRefGoogle ScholarPubMed
Clifford, G.D. and Tarassenko, L., Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. Biomedical Engineering, IEEE Transactions on, 2005. 52(4): p. 630–638.CrossRefGoogle ScholarPubMed
Press, W.H. and Rybicki, G.B., Fast algorithm for spectral analysis of unevenly sampled data. The Astrophysical Journal, 1989. 338: p. 277–280.CrossRefGoogle Scholar

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