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Ballistocardiography

Chapter

Abstract

Ballistocardiogram (BCG) is the record of mechanical forces exerted by the pumping heart. Movement of the blood volume through the heart chambers and ejection to the arteries causes recoil forces of the body which can be detected with appropriate sensors. It represents the rhythmic activity and the normality of the heart. Several types of sensors can be applied to measure BCG. Among those sensors, accelerometers or film-type sensors are representative, which can measure BCG rather easily without attaching sensors directly to the body surface. These sensors have also merit of easily combining into our daily using devices like chairs, weigh scales, and beds. Usually heart rate and heart rate variability are retrieved from BCG for further application. Measured BCGs are widely used for daily healthcare monitoring including sleep evaluation based on its characteristics of unobtrusiveness.

Keywords

Ballistocardiogram Accelerometer Film-type sensors Heart rate variability Sleep evaluation Unobtrusive Healthcare 

References

  1. 1.
    Gordon, J. W. (1877). Certain molar movements of the human body produced by the circulation of the blood. Journal of Anatomy Physiology, 11, 533–536.Google Scholar
  2. 2.
    Starr, I., Rawson, A. J., & Schroeder, H. A. (1938). Apparatus for recording the heart’s recoil and the blood’s impacts in man (ballistocardiograph), experiments on the principles involved, records in normal and abnormal conditions. The American Journal of Physiology, 123, 195.Google Scholar
  3. 3.
    Starr, I., Rawson, A. J., Schroeder, H. A., & Joseph, N. R. (1939). Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function from the heart’s recoil and the blood’s impact; the ballistocardiogram. The American Journal of Physiology, 127, 1.Google Scholar
  4. 4.
    Starr, I., & Schroeder, H. A. (1940). Ballistocardiogram. II. Normal standards, abnormalities commonly found in diseases of the heart and circulation, and their significance. The Journal of Clinical Investigation, 19, 437–450.CrossRefGoogle Scholar
  5. 5.
    Pinheiro, E., Postolache, O., & Girao, P. (2010). Theory and developments in an unobtrusive cardiovascular system representation: Ballistocardiography. Open Biomedical Engineering Journal, 4, 201–216.CrossRefGoogle Scholar
  6. 6.
    Kim, C. S., Ober, S. L., McMurtry, M. S., Finegan, B. A., Inan, O. T., Mukkamala, R., & Hahn, J. O. (2016). Ballistocardiogram: Mechanism and potential for unobtrusive cardiovascular health monitoring. Scientific Reports, 6, 31297–31302.CrossRefGoogle Scholar
  7. 7.
    Eblen-Zajjur, A. (2003). A simple ballistocardiographic system for a medical cardiovascular physiology course. Advances in Physiology Education, 27, 224–229.CrossRefGoogle Scholar
  8. 8.
    Dock, W., & Taubman, F. (1949). Some technics for recording the ballistocardiogram directly from the body. The American Journal of Medicine, 7, 751–755.CrossRefGoogle Scholar
  9. 9.
    Alihanka, J., & Vaahtoranta, K. (1979). A static charge sensitive bed. A new method for recording body movement during sleep. Electroencephalography and Clinical Neurophysiology, 46, 731–734.CrossRefGoogle Scholar
  10. 10.
    Krahl, V. E. (1950). The electronic stain gauge ballistocardiography. American Heart Journal., 39, 161–173.CrossRefGoogle Scholar
  11. 11.
    Gonzalez-Landaeta, R., Casas, O., & Pallas-Areny, R. (2008). Heart rate detection from an electronic weighing scale. Physiological Measurements, 29, 979–988.CrossRefGoogle Scholar
  12. 12.
    Inan, O. T., Etemadi, M., Wiard, R. M., Giovangrandi, L., & Kovacs, G. T. A. (2009). Robust ballistocardiogram acquisition for home monitoring. Physiological Measurements, 30, 169–185.CrossRefGoogle Scholar
  13. 13.
    Shin, J. H., Hwang, S. H., Chang, M. H., & Park, K. S. (2011). Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise. Physiological Measurements, 32, 1239–1264.CrossRefGoogle Scholar
  14. 14.
    Shin, J. H., Lee, K. M., & Park, K. S. (2009). Non-constrained monitoring of systolic blood pressure on a weighing scale. Physiological Measurements, 30, 679–693.CrossRefGoogle Scholar
  15. 15.
    Choi, B. H., Chung, G. S., Lee, J. S., Jeong, D. U., & Park, K. S. (2009). Slow-wave sleep estimation on a load cell installed bed: A non-constrained method. Physiological Measurements, 30, 1163–1170.CrossRefGoogle Scholar
  16. 16.
    Yamakoshi, K., Kuroda, M., Tanak, S., Yamaguchi, I., & Kawarada, A. (1996). Non-conscious and automatic acquisition of body and excreta weight together with ballistocardiogram in a lavatory. Proceeding of 18th IEEE EMBC, IEEE Press.Google Scholar
  17. 17.
    Elliott, R. V., Packard, R. G., & Kyrazis, D. T. (1954). Acceleration ballistocardiography: Design, construction, and application of a new instrument. Circulation, 9, 281–291.CrossRefGoogle Scholar
  18. 18.
    Prisk, G. K., Verhaeghe, S., Padeke, D., Hamacher, H., & Pavia, M. (2001). Three-dimensional ballistocardiography and respiratory motion in sustained microgravity. Aviation, Space, and Environmental Medicine, 72, 1067–1074.Google Scholar
  19. 19.
    Vogt, E., MacQuarrie, D., & Neary, J. P. (2012). Using ballistocardiography to measure cardiac performance: A brief review of its history and future significance. Clinical Physiology and Functional Imaging, 32, 415–420.CrossRefGoogle Scholar
  20. 20.
    Rajala, S., & Lekkala, J. (2012). Film-type sensor materials PVDF and EMFi in measurement of cardiorespiratory signals: A review. IEEE Sensor Journal, 12, 439–446.CrossRefGoogle Scholar
  21. 21.
    Rajala, S., & Lekkala, J. (2010). PVDF and EMFi sensor materials: A comparative study. Procedia Engineering, 5, 862–865.CrossRefGoogle Scholar
  22. 22.
    Wang, J. Q., Zheng, C. X., Jin, X. J., Lu, G. H., & Ni, A. S. (2004). Study on a non-contact life parameter detection system using millimeter wave. Journal of Space Medicine & Medical Engineering of China, 17, 157–161.Google Scholar
  23. 23.
    Lazaro, A., Girbau, D., & Villarino, R. (2010). Analysis of vital signs monitoring using an IR-UWB radar. Progress in Electromagnetics Research, 100, 265–284.CrossRefGoogle Scholar
  24. 24.
    Jansen, B. H., Larson, B. H., & Shankar, K. (1991). Monitoring of the ballistocardiogram with the static charge sensitive bed. IEEE Transactions on Biomedical Engineering, 38(8), 748–751.CrossRefGoogle Scholar
  25. 25.
    Shin, J. H., Choi, B. H., Lim, Y. G., Jeong, D. U., & Park, K. S. (2008). Automatic ballistocardiogram (BCG) beat detection using a template matching approach. Proceedings of the IEEE 30th annual international conference Engineering Medical Biology Society, pp. 1144–1146.Google Scholar
  26. 26.
    Brueser, C., Stadlthanner, K., Waele, S. D., & Leonhardt, S. (2011). Adaptive beat-to-beat heart rate estimation in ballistocardiograms. IEEE Transactions on Information Technology in Biomedicine, 15, 778–786.CrossRefGoogle Scholar
  27. 27.
    Paalasmaa, J., Toivonen, H., & Partinen, M. (2015). Adaptive heartbeat modelling for beat-to-beat heart rate measurement in ballistocardiograms. IEEE Journal Biomedical Health Informatics, 19, 1945–1952.CrossRefGoogle Scholar
  28. 28.
    Friedrich, D., Aubert, X. L., Fuhr, H., & Brauers, A. (2010). Heart rate estimation on a beat-to-beat basis via ballistocardiography—A hybrid approach. Proceedings: IEEE 32nd annual international conference Engineering in Medicine and Biology Society.Google Scholar
  29. 29.
    Sprager, S., & Zazula, D. (2014). Optimization of heartbeat detection in fiber optic unobtrusive measurements by using maximum a posteriori probability estimation. IEEE Journal of Biomedical and Health Informatics, 18, 1161–1168.CrossRefGoogle Scholar
  30. 30.
    Zhu, X., Chen, W., Nemoto, T., Kanemitsu, Y., Kitamura, K., Yamakoshi, K., & Wei, D. (2006). Real-time monitoring of respiration rhythm and pulse rate during sleep. IEEE Transactions on Biomedical in Engineering, 53, 2553–2563.CrossRefGoogle Scholar
  31. 31.
    Brueser, C., Kortelainen, J. M., Winter, S., Tenhunen, M., Parkka, J., & Leonhardt, S. (2015). Improvement of force-sensor-based heart rate estimation using multi-channel data fusion. IEEE Journal of Biomedical and Health Informatics, 19, 227–235.CrossRefGoogle Scholar
  32. 32.
    Brueser, C., Winter, S., & Leonhardt, S. (2013). Robust inter-beat interval estimation in cardiac vibration signals. Physiological Measurements, 34, 123–138.CrossRefGoogle Scholar
  33. 33.
    Zhu, Y., Zhang, H., Jayachandran, M., Ng, A. K., Biswas, J., & Chen, Z. (2013). Ballistocardiography with fiber optic sensor in headrest position: A feasibility study and a new processing algorithm. Proceedings of the 35th IEEE annual international conference of the Engineering Medicine and Biology Society, pp. 5203–5206.Google Scholar
  34. 34.
    Kortelainen, J. M., & Virkkala, J. (2007). FFT averaging of multichannel BCG signals from bed mattress sensor to improve estimation of heart beat interval. Proceedings of the IEEE 29th annual international conference Engineering of the Medicine and Biology Society, pp. 6685–6688.Google Scholar
  35. 35.
    Lim, Y., Hong, K., Kim, K. K., Shin, J. H., Lee, S. M., Chung, G. S., Baek, H. J., Jeong, D.-U., & Park, K. S. (2011). Monitoring physiological signals using nonintrusive sensors installed in daily life equipment. Biomedical Engineering Letters, 1, 11–20.CrossRefGoogle Scholar
  36. 36.
    X. Zhu, W. Chen, K. Kitamura, T. Nemoto. (2012). Comparison of pulse rate variability indices estimated from pressure signal and photoplethysmogram. Proceedings of the IEEE international conference Biomedical and Health Informatics, Hong Kong, Shenzhen, pp. 867–870.Google Scholar
  37. 37.
    Alihanka, J., Vaahtoranta, K., & Saarikivi, I. (1981). A new method for long term monitoring of the ballistocardiogram, heart rate, and respiration. The American Journal of Physiology, 240, R384–R392.Google Scholar
  38. 38.
    Chee, Y., Han, J., & Park, K. S. (2005). Air mattress sensor system with balancing tube for unconstrained measurement of respiration and heart beat movements. Physiological Measurements, 26, 413–422.CrossRefGoogle Scholar
  39. 39.
    Mack, D. C., Patrie, J. T., Suratt, P. M., Felder, R. A., & Alwan, M. (2009). Development and preliminary validation of heart rate and breathing rate detection using a passive, ballistocardiography-based sleep monitoring system. IEEE Transactions on Information Technology Biomedicine, 13, 111–120.CrossRefGoogle Scholar
  40. 40.
    Vehkaoja, A., Rajala, S., Kumpulainen, P., & Lekkala, J. (2013). Correlation approach for the detection of the heartbeat intervals using force sensors placed under the bed posts. Journal of Medical Engineering & Technology, 37, 327–333.CrossRefGoogle Scholar
  41. 41.
    Sprager, S., & Zazula, D. (2012). Heartbeat and respiration detection from optical interferometric signals by using a multimethod approach. IEEE Transactions on Biomedical Engineering, 59, 2922–2929.CrossRefGoogle Scholar
  42. 42.
    Dziuda, L., & Skibniewski, F. (2012). Monitoring respiration and cardiac activity using fiber Bragg grating-based sensor. IEEE Transactions on Biomedical Engineering, 59, 1934–1942.CrossRefGoogle Scholar
  43. 43.
    Paalasmaa,J., Waris, M., Toivonen, H., Leppakorpi, L., & Partinen, M. (2012). Unobtrusive online monitoring of sleep at home. Proceedings of the IEEE annual international conference Engineering Medicine and Biology Society, San Diego, pp. 3784–3788.Google Scholar
  44. 44.
    Wang, F., Tanaka, M., & Chonan, S. (2003). Development of a PVDF piezopolymer sensor for unconstrained in-sleep cardiorespiratory monitoring. Journal of Intelligent Material Systems and Structures, 14, 185–190.CrossRefGoogle Scholar
  45. 45.
    Watanabe, K., Watanabe, T., Watanabe, H., Ando, H., Ishikawa, T., & Kobayashi, K. (2005). Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method. IEEE Transactions on Biomedical Engineering, 52, 2100–2107.CrossRefGoogle Scholar
  46. 46.
    Jung, D. W., Hwang, S. H., Yoon, H. N., Lee, Y.-J. G., Jeong, D.-U., & Park, K. S. (2014). Nocturnal awakening and sleep efficiency estimation using unobtrusively measured ballistocardiogram. IEEE Transactions on Biomedical Engineering, 61, 131–138.CrossRefGoogle Scholar
  47. 47.
    Kortelainen, J. M., Mendez, M. O., Bianchi, A. M., Matteucci, M., & Cerutti, S. (2010). Sleep staging based on signals acquired through bed sensor. IEEE Transactions on Information Technology in Biomedicine, 14, 776–785.CrossRefGoogle Scholar
  48. 48.
    Migliorini, M., Bianchi, A. M., Nistico, D., Kortelainen, J., Arce-Santana, E., Cerutti, S., & Mendez, M. O. (2010). Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors. Proceedings of the IEEE annual international conference Engineering in Medicine and Biology Society, pp. 3273–3276.Google Scholar
  49. 49.
    Jung, D. W., Hwang, S. H., Chung, G. S., Lee, Y. J., Jeong, D. U., & Park, K. S. (2013). Estimation of sleep onset latency based on the blood pressure regulatory reflex mechanism. IEEE Journal of Biomedical and Health Informatics, 17, 539–544.CrossRefGoogle Scholar
  50. 50.
    Jung, D. W., Lee, Y. J., Jeong, D. U., & Park, K. S. (2017a). New predictors of sleep efficiency. Chronobiology International, 34, 93–104.Google Scholar
  51. 51.
    Hwang, S. H., Lee, H. J., Yoon, H. N., Jung, D. W., Lee, Y. J. G., Lee, Y. J., Jeong, D. U., & Park, K. S. (2014). Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor. IEEE Transactions on Biomedical Engineering, 61, 2125–2134.CrossRefGoogle Scholar
  52. 52.
    Jung, D. W., Hwang, S. H., Lee, Y. J., Jeong, D. U., & Park, K. S. (2017b). Apnea-hypopnea index prediction using electrocardiogram acquired during sleep-onset period. IEEE Transactions on Biomedical Engineering, 64, 295–301.Google Scholar
  53. 53.
    Lee, W. K., Yoon, H., Han, C., Joo, K. M., & Park, K. S. (2016). Physiological signal monitoring bed for infants based on load-cell sensors. Sensors, 16, 409.CrossRefGoogle Scholar
  54. 54.
    Koivistoinen, T., Junnila, S., Varri, A., & Koobi, T. (2004). A new method for measuring the ballistocardiogram using EMFi sensors in a normal chair. Proceedings of the IEEE 26th EMBS, San Francisco, pp. 2026–2029.Google Scholar
  55. 55.
    Su, J., Zhu, X., Zhang, X., & Tang, J. (2009). Ballistocardiogram measurement system using three load-cell sensors platform in chair, proceedings of the IEEE 31th EMBS, Tianjin.Google Scholar
  56. 56.
    Postolache, O. A., Silva Girao, P. M. B., Mendes, J., Pinheiro, E. C., & Postolache, G. (2010). Physiological parameters measurement based on wheelchair embedded sensors and advanced signal processing. IEEE Transactions on Instrumentation and Measurement, 59, 2564–2574.CrossRefGoogle Scholar
  57. 57.
    Baek, H. J., Chung, G. S., Kim, K. K., & Park, K. S. (2012). A smart health monitoring chair for nonintrusive measurement of biological signals. IEEE Transactions on Information Technology in Biomedicine, 16, 150–158.CrossRefGoogle Scholar
  58. 58.
    Jankowska, E. A., Ponikowski, P., Piepoli, M. F., Banasiak, W., Anker, S. D., & Poole-Wilson, P. A. (2006). Autonomic imbalance and immune activation in chronic heart failure-pathophysiological links. Cardiovascular Research, 70, 434–445.CrossRefGoogle Scholar
  59. 59.
    Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology, 141, 122–131.CrossRefGoogle Scholar
  60. 60.
    Stein, P. K., Rich, M. W., Rottman, J. N., & Kleiger, R. E. (1995). Stability of index of heart rate variability in patients with congestive heart failure. American Heart Journal, 129, 975–981.CrossRefGoogle Scholar
  61. 61.
    Saul, J. P., Arai, Y., Berger, R. D., Lilly, L. S., Colucci, W. S., & Cohen, R. J. (1988). Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis. The American Journal of Cardiology, 61, 1292–1299.CrossRefGoogle Scholar
  62. 62.
    Bigger Jr., J. T., Fleiss, J. L., Steinman, R. C., Rolnitzky, L. M., Schneider, W. J., & Stein, P. K. (1995). RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation, 91, 1936–1943.CrossRefGoogle Scholar
  63. 63.
    Weber, F., Schneider, H., von Arnim, T., & Urbaszek, W. (1999). Heart rate variability and ischaemia in patients with coronary heart disease and stable angina pectoris; influence of drug therapy and prognostic value. TIBBS Investigators Group. Total Ischemic Burden Bisoprolol Study. European Heart Journal, 20, 38–50.CrossRefGoogle Scholar
  64. 64.
    Burger, A. J., Charlamb, M., Weinrauch, L. A., & D’Elia, J. A. (1997). Short- and long-term reproducibility of heart rate variability in patients with long-standing type I diabetes mellitus. The American Journal of Cardiology, 80, 1198–1202.CrossRefGoogle Scholar
  65. 65.
    Nolan, J., Flapan, A. D., Goodfield, N. E., Prescott, R. J., Bloomfield, P., Neilson, J. M., & Ewing, D. J. (1996). Measurement of parasympathetic activity from 24h ambulatory electrocardiograms and its reproducibility and sensitivity in normal subjects, patients with symptomatic myocardial ischemia, and patients with diabetes mellitus. The American Journal of Cardiology, 77, 154–158.CrossRefGoogle Scholar
  66. 66.
    Ewing, D. J., Neilson, J. M., Shapiro, C. M., Stewart, J. A., & Reid, W. (1991). Twenty four hour heart rate variability: Effects of posture, sleep, and time of day in healthy controls and comparison with bedside tests of autonomic function in diabetic patients. British Heart Journal, 65, 239–244.CrossRefGoogle Scholar
  67. 67.
    Konrady, A. O., Rudomanov, O. G., Yacovleva, O. I., & Shlyakhto, E. V. (2001). Power spectral components of heart rate variability in different types of cardiac remodelling in hypertensive patients. Medical Science Monitor, 7, 58–63.Google Scholar
  68. 68.
    Forslund, L., Bjorkander, I., Ericson, M., Held, C., Kahan, T., Rehnqvist, N., & Hjemdahl, P. (2002). Prognostic implications of autonomic function assessed by analyses of catecholamines and heart rate variability in stable angina pectoris. Heart, 87, 415–422.CrossRefGoogle Scholar
  69. 69.
    Lanza, G. A., Pedrotti, P., Rebuzzi, A. G., Pasceri, V., Quaranta, G., & Maseri, A. (1997). Usefulness of the addition of heart rate variability to Holter monitoring in predicting in-hospital cardiac events in patients with unstable angina pectoris. The American Journal of Cardiology, 80, 263–267.CrossRefGoogle Scholar
  70. 70.
    van Boven, A. J., Jukema, J. W., Haaksma, J., Zwinderman, A. H., Crijns, H. J., & Lie, K. I. (1998). Depressed heart rate variability is associated with events in patients with stable coronary artery disease and preserved left ventricular function. REGRESS Study Group. American Heart Journal, 135, 571–576.CrossRefGoogle Scholar
  71. 71.
    Kleiger, R. E., Miller, J. P., Bigger Jr., J. T., & Moss, A. J. (1987). Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. The American Journal of Cardiology, 59, 256–262.CrossRefGoogle Scholar
  72. 72.
    Bigger Jr., J. T., Fleiss, J. L., Rolnitzky, L. M., & Steinman, R. C. (1993). Frequency domain measures of heart period variability to assess risk late after myocardial infarction. Journal of the American College of Cardiology, 21, 729–736.CrossRefGoogle Scholar
  73. 73.
    Tsuji, H., Larson, M. G., Venditti Jr., F. J., Manders, E. S., Evans, J. C., Feldman, C. L., & Levy, D. (1996). Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation, 94, 2850–2855.CrossRefGoogle Scholar
  74. 74.
    Tsuji, H., Venditti Jr., F. J., Manders, E. S., Evans, J. C., Larson, M. G., Feldman, C. L., & Levy, D. (1994). Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation, 90, 878–883.CrossRefGoogle Scholar
  75. 75.
    Wiens, A. D., Etemadi, M., Roy, S., Klein, L., & Inan, O. T. (2015). Toward continuous, noninvasive assessment of ventricular function and hemodynamics: Wearable ballistocardiography. IEEE Journal of Biomedical and Health Informatics, 19, 1435–1442.CrossRefGoogle Scholar
  76. 76.
    He, D. D., Winokur, E. S., & Sodini, C. G. (2012). An ear-worn continuous ballistocardiogram (BCG) sensor for cardiovascular monitoring, proceedings of the IEEE 34th EMBS.Google Scholar
  77. 77.
    He, D. D., Winokur, E. S., & Sodini, C. G. (2015). An ear-worn vital signs monitor. IEEE Transactions on Biomedical Engineering, 62, 2547–2552.CrossRefGoogle Scholar
  78. 78.
    Noh, S., Yoon, C., Hyun, E., Yoon, H., Chung, T., Park, K., & Kim, H. (2014). Ferroelectret film-based patch-type sensor for continuous blood pressure monitoring. Electronics Letters, 50, 143–144.CrossRefGoogle Scholar
  79. 79.
    Wong, M. Y. M., Pickwell-MacPherson, E., Zhang, Y. T., & Cheng, J. C. (2011). The effects of pre-ejection period on post-exercise systolic blood pressure estimation using the pulse arrival time technique. European Journal of Applied Physiology, 111, 135–144.CrossRefGoogle Scholar
  80. 80.
    Inan, O. T., Migeotte, P. F., Park, K. S., Etemadi, M., Tavakolian, K., Casanella, R., Zanetti, J., Tank, J., Funtova, I., Prisk, G. K., & Rienzo, M. D. (2015). Ballistocardiography and seismocardiography: A review of recent advances. IEEE Journal of Biomedical Health Informatics, 19, 1414–1427.CrossRefGoogle Scholar
  81. 81.
    Guo, H., Cao, X., Wu, J., & Tang, J. (2013). Ballistocardiogram-based person identification using correlation analysis. IFMBE proceedings of world congress on Medical Physics and Biomedical Engineering, Beijing, pp. 570–573.Google Scholar
  82. 82.
    Vural, E., Simske,S., & Schuckers, S. (2013). Verification of individuals from accelerometer measures of cardiac chest movements. Proceeding of international conference of the BIOSIG Special Interest Group, pp. 1–8.Google Scholar
  83. 83.
    Goedhard, W. J. A. (1979). Ballistocardiography: Past, present and future. Bibliotheca Cardiologica, 37, 27–45.Google Scholar
  84. 84.
    Cournand, A., Ranges, H. A., & Riley, R. L. (1942). Comparison of results of the normal ballistocardiogram and a direct Fick method in measuring the cardiac output in man. The Journal of Clinical Investigation, 21, 287–294.CrossRefGoogle Scholar
  85. 85.
    Pollock, P. (1957). Ballistocardiography a clinical review. Canadian Medical Association Journal, 76, 778–783.Google Scholar
  86. 86.
    Mandelbaum, H., & Mandelbaum, R. A. (1953). Studies utilizing the portable electromagnetic BCG. IV. The clinical significance of serial BCGs following acute myocardial infarction. Circulation, 7, 910–915.CrossRefGoogle Scholar
  87. 87.
    Mackinson, D. H. (1950). Changes in the ballistocardiogram after exercise in normal and abnormal. Circulation, 2, 186–196.CrossRefGoogle Scholar
  88. 88.
    Soames, R. W., & Atha, J. (1982). Three-dimensional ballistocardiographic responses to changes of posture. Clinical Physics and Physiological Measurement, 3, 169–177.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringSeoul National University College of MedicineSeoulSouth Korea
  2. 2.Interdisciplinary Program of BioengineeringSeoul National University Graduate SchoolSeoulSouth Korea

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