Skip to main content
SpringerLink
Log in

Electrocardiogram: Acquisition and Analysis for Biological Investigations and Health Monitoring

  • Chapter
  • First Online:
Book cover Interfacing Bioelectronics and Biomedical Sensing

Abstract

Electrocardiogram (ECG or EKG) was introduced since 1893 by Einthoven, and it has been used for decades in clinical settings for vital sign monitoring as well as cardiac assessment. The ECG signal with its unique characteristic waves of P waves, QRS complexes, and T waves holds important information about the functionalities of the heart. In recent years, advances in electronics and telecommunications have paved the way for out-of-clinic ECG acquisition and monitoring. The rise of advanced data science techniques, such as machine learning, has further opened doors for distanced, home-based, and automated diagnoses. In parallel, micro- and nanotechnology has enabled significant strides in biological investigations using small animal models, such as zebrafish and mouse, uncovering underlying mechanisms of numerous biological processes. In this chapter, we first introduce the basics of electrocardiogram and the methods for acquisition; and then systems used with zebrafish and humans are discussed. Artificial intelligence, specifically machine learning, is brought into the discussion with an emphasis on the use of convolutional neuron networks for classifying ECG patterns of arrhythmic zebrafish mutants. Finally, the chapter recapitulates with the necessity of translating findings from animal research for use with humans as well as a body sensor network with multimodal sensors which may reveal unprecedented connections among physiological parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. W. H. Organization. (2018). The top 10 causes of death. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

  2. Hsieh, P. C., Segers, V. F., Davis, M. E., MacGillivray, C., Gannon, J., Molkentin, J. D., et al. (2007, August). Evidence from a genetic fate-mapping study that stem cells refresh adult mammalian cardiomyocytes after injury. Nature Medicine, 13, 970–974.

    Article  Google Scholar 

  3. Bergmann, O., Bhardwaj, R. D., Bernard, S., Zdunek, S., Barnabe-Heider, F., Walsh, S., et al. (2009, April 3). Evidence for cardiomyocyte renewal in humans. Science, 324, 98–102.

    Article  Google Scholar 

  4. Bersell, K., Arab, S., Haring, B., & Kuhn, B. (2009, Jul 23). Neuregulin1/ErbB4 signaling induces cardiomyocyte proliferation and repair of heart injury. Cell, 138, 257–270.

    Article  Google Scholar 

  5. Giudicessi, J. R., & Ackerman, M. J. (2013, Janunary). Genetic testing in heritable cardiac arrhythmia syndromes: differentiating pathogenic mutations from background genetic noise. Current Opinion in Cardiology, 28, 63–71.

    Article  Google Scholar 

  6. Haïssaguerre, M., Derval, N., Sacher, F., Jesel, L., Deisenhofer, I., de Roy, L., et al. (2008). Sudden cardiac arrest associated with early repolarization. New England Journal of Medicine, 358, 2016–2023.

    Article  Google Scholar 

  7. Lubitz, S. A., & Ellinor, P. T. (2015, May). Next-generation sequencing for the diagnosis of cardiac arrhythmia syndromes. Heart Rhythm : The Official Journal of the Heart Rhythm Society, 12, 1062–1070.

    Article  Google Scholar 

  8. Christophersen, I. E., Magnani, J. W., Yin, X., Barnard, J., Weng, L.-C., Arking, D. E., et al. Fifteen genetic loci associated with the electrocardiographic P wave clinical perspective. Circulation: Genomic and Precision Medicine, 10, e001667, 2017.

    Google Scholar 

  9. Nielsen, J. B., Fritsche, L. G., Zhou, W., Teslovich, T. M., Holmen, O. L., Gustafsson, S., et al. (2017). Genome-wide study of atrial fibrillation identifies seven risk loci and highlights biological pathways and regulatory elements involved in cardiac development. The American Journal of Human Genetics, 102(1), 103–115.

    Article  Google Scholar 

  10. Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., et al. (2017). 10 years of GWAS discovery: biology, function, and translation. The American Journal of Human Genetics, 101, 5–22.

    Article  Google Scholar 

  11. Pajkrt, E., Weisz, B., Firth, H. V., & Chitty, L. S. (2004). Fetal cardiac anomalies and genetic syndromes. Prenatal Diagnosis, 24, 1104–1115.

    Article  Google Scholar 

  12. Miniño, A. M., Heron, M. P., Murphy, S. L., & Kochanek, K. D.. (2007). Deaths: Final data for 2004, ed: Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.

    Google Scholar 

  13. Araneta, G., Rosario, M., Schlangen, K. M., Edmonds, L. D., Destiche, D. A., Merz, R. D., et al. (2003). Prevalence of birth defects among infants of Gulf War veterans in Arkansas, Arizona, California, Georgia, Hawaii, and Iowa, 1989–1993. Birth Defects Research Part A: Clinical and Molecular Teratology, 67, 246–260.

    Article  Google Scholar 

  14. Donovan, J. W., Maclennan, R., & Adena, M. (1984). Vietnam service and the risk of congenital anomalies. A case-control study. Obstetrical & Gynecological Survey, 39, 24–25.

    Article  Google Scholar 

  15. Lombó, M., Fernández-Díez, C., González-Rojo, S., Navarro, C., Robles, V., & Herráez, M. P. (2015). Transgenerational inheritance of heart disorders caused by paternal bisphenol A exposure. Environmental Pollution, 206, 667–678.

    Article  Google Scholar 

  16. Poss, K. D., Wilson, L. G., & Keating, M. T. (Dec 13 2002). Heart regeneration in zebrafish. Science, 298, 2188–2190.

    Article  Google Scholar 

  17. Raya, A., Consiglio, A., Kawakami, Y., Rodriguez-Esteban, C., & Izpisua-Belmonte, J. C. (2004). The zebrafish as a model of heart regeneration. Cloning and Stem Cells, 6, 345–351.

    Article  Google Scholar 

  18. Poss, K. D., Wilson, L. G., & Keating, M. T. (2002). Heart regeneration in zebrafish. Science, 298, 2188–2190.

    Article  Google Scholar 

  19. Raya, Á., Consiglio, A., Kawakami, Y., Rodriguez-Esteban, C., & Izpisúa-Belmonte, J. C. (2004). The zebrafish as a model of heart regeneration. Cloning and Stem Cells, 6, 345–351.

    Article  Google Scholar 

  20. Porrello, E. R., Mahmoud, A. I., Simpson, E., Hill, J. A., Richardson, J. A., Olson, E. N., et al. (2011). Transient regenerative potential of the neonatal mouse heart. Science, 331, 1078–1080.

    Article  Google Scholar 

  21. Huang, G. N., Thatcher, J. E., McAnally, J., Kong, Y., Qi, X., Tan, W., et al. (2012). C/EBP transcription factors mediate epicardial activation during heart development and injury. Science, 338, 1599–1603.

    Article  Google Scholar 

  22. Kikuchi, K., Holdway, J. E., Werdich, A. A., Anderson, R. M., Fang, Y., Egnaczyk, G. F., et al. (2010). Primary contribution to zebrafish heart regeneration by gata4+ cardiomyocytes. Nature, 464, 601–605.

    Article  Google Scholar 

  23. Lien, C. L., Harrison, M. R., Tuan, T. L., & Starnes, V. A. (2012). Heart repair and regeneration: Recent insights from zebrafish studies. Wound Repair and Regeneration, 20, 638–646.

    Article  Google Scholar 

  24. Narula, J., Haider, N., Virmani, R., DiSalvo, T. G., Kolodgie, F. D., Hajjar, R. J., et al. (1996). Apoptosis in myocytes in end-stage heart failure. New England Journal of Medicine, 335, 1182–1189.

    Article  Google Scholar 

  25. Olivetti, G., Abbi, R., Quaini, F., Kajstura, J., Cheng, W., Nitahara, J. A., et al. (1997). Apoptosis in the failing human heart. New England Journal of Medicine, 336, 1131–1141.

    Article  Google Scholar 

  26. Rosenzweig, A. (2012). Cardiac regeneration. Science, 338, 1549–1550.

    Article  Google Scholar 

  27. Forsburg, S. L. (Sep 2001). The art and design of genetic screens: Yeast. Nature Reviews. Genetics, 2, 659–668.

    Article  Google Scholar 

  28. St Johnston, D. (Mar 2002). The art and design of genetic screens: Drosophila melanogaster. Nature Reviews. Genetics, 3, 176–188.

    Article  Google Scholar 

  29. Jorgensen, E. M., & Mango, S. E. (May 2002). The art and design of genetic screens: Caenorhabditis elegans. Nature Reviews. Genetics, 3, 356–369.

    Article  Google Scholar 

  30. Angel, P. M., Nusinow, D., Brown, C. B., Violette, K., Barnett, J. V., Zhang, B., et al. (2011, December 22). Networked-based characterization of extracellular matrix proteins from adult mouse pulmonary and aortic valves. Journal of Proteome Research, 10, 812–823.

    Article  Google Scholar 

  31. Lenning, M., Fortunato, J., Le, T., Clark, I., Sherpa, A., Yi, S., et al. (2018). Real-time monitoring and analysis of Zebrafish electrocardiogram with anomaly detection. Sensors, 18, 61.

    Article  Google Scholar 

  32. Ding, Y., Liu, W., Deng, Y., Jomok, B., Yang, J., Huang, W., et al. (2013, February 15). Trapping cardiac recessive mutants via expression-based insertional mutagenesis screening. Circulation Research, 112, 606–617.

    Article  Google Scholar 

  33. Ding, Y., Long, P. A., Bos, J. M., Shih, Y. H., Ma, X., Sundsbak, R. S., et al. (2016). A modifier screen identifies DNAJB6 as a cardiomyopathy susceptibility gene. JCI Insight, 1(14), e88797. https://doi.org/10.1172/jci.insight.88797.

    Article  Google Scholar 

  34. Clark, K. J., Balciunas, D., Pogoda, H. M., Ding, Y., Westcot, S. E., Bedell, V. M., et al. (2011, Jun). In vivo protein trapping produces a functional expression codex of the vertebrate proteome. Nature Methods, 8, 506–515.

    Article  Google Scholar 

  35. Mathur, P., & Guo, S. (2010). Use of zebrafish as a model to understand mechanisms of addiction and complex neurobehavioral phenotypes. Neurobiology of Disease, 40, 66–72.

    Article  Google Scholar 

  36. Ninkovic, J., & Bally-Cuif, L. (2006). The zebrafish as a model system for assessing the reinforcing properties of drugs of abuse. Methods, 39, 262–274.

    Article  Google Scholar 

  37. Knecht, A. L., Truong, L., Marvel, S. W., Reif, D. M., Garcia, A., Lu, C., et al. (2017). Transgenerational inheritance of neurobehavioral and physiological deficits from developmental exposure to benzo [a] pyrene in zebrafish. Toxicology and Applied Pharmacology, 329, 148–157.

    Article  Google Scholar 

  38. Cao, H., Yu, F., Zhao, Y., Zhang, X., Tai, J., Lee, J., et al. (2014). Wearable multi-channel microelectrode membranes for elucidating electrophysiological phenotypes of injured myocardium. Integrative Biology: Quantitative Biosciences from Nano to Macro, 6, 789–795.

    Article  Google Scholar 

  39. Yu, F., Zhao, Y., Gu, J., Quigley, K. L., Chi, N. C., Tai, Y.-C., et al. (2012). Flexible microelectrode arrays to interface epicardial electrical signals with intracardial calcium transients in zebrafish hearts. Biomedical Microdevices, 14, 357–366.

    Article  Google Scholar 

  40. Forouhar, A., Hove, J., Calvert, C., Flores, J., Jadvar, H., & Gharib, M. (2004). Electrocardiographic characterization of embryonic zebrafish. In Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE, 2004, pp. 3615–3617.

    Google Scholar 

  41. Milan, D. J., & MacRae, C. A. (2005). Animal models for arrhythmias. Cardiovascular Research, 67, 426–437.

    Article  Google Scholar 

  42. Sun, P., Zhang, Y., Yu, F., Parks, E., Lyman, A., Wu, Q., et al. (2009). Micro-electrocardiograms to study post-ventricular amputation of zebrafish heart. Annals of Biomedical Engineering, 37, 890–901.

    Article  Google Scholar 

  43. Yu, F., Huang, J., Adlerz, K., Jadvar, H., Hamdan, M. H., Chi, N., et al. (2010). Evolving cardiac conduction phenotypes in developing zebrafish larvae: Implications to drug sensitivity. Zebrafish, 7, 325–331.

    Article  Google Scholar 

  44. Shier, D. N., Butler, J. L., & Lewis, R. (2011). Hole’s essentials of human anatomy and physiology. McGraw-Hill Higher Education. Pennsylvania Plaza New York City.

    Google Scholar 

  45. Widmaier, E. P., Raff, H., & Strang, K. T. (2006). Vander’s human physiology: The mechanisms of body function (Vol. 10, pp. 454–455). New York: McGraw-Hill.

    Google Scholar 

  46. Natalie casebook. Available: http://www.nataliescasebook.com/tag/cardiac-action-potentials

  47. Klabunde, R. E. (2011). Cardiovascular physiology concepts: Wolters Kluwer Health/Lippincott Williams & Wilkins.

    Google Scholar 

  48. Sharma, M., Barbosa, K., Ho, V., Griggs, D., Ghirmai, T., Krishnan, S. K., et al. (2017). Cuff-less and continuous blood pressure monitoring: A methodological review. Technologies, 5, 21.

    Article  Google Scholar 

  49. Le, T., Han, H. D., Hoang, T. H., Nguyen, V. C., & Nguyen, C. K. (2016). A low cost mobile ECG monitoring device using two active dry electrodes. In 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), 2016, pp. 271–276.

    Google Scholar 

  50. Sharma, M., Ritchie, P., Ghirmai, T., Cao, H., & Lau, M. P. (2017) Unobtrusive acquisition and extraction of fetal and maternal ECG in the home setting. In SENSORS, 2017 IEEE, 2017, pp. 1–3.

    Google Scholar 

  51. Neuman, M. R. (1998). Biopotential amplifiers. In J. G. Webster (Ed.), Medical instrumentation: Application and design (pp. 233–286). New York: Wiley.

    Google Scholar 

  52. Merletti, R., Botter, A., Troiano, A., Merlo, E., & Minetto, M. A. (2009). Technology and instrumentation for detection and conditioning of the surface electromyographic signal: State of the art. Clinical Biomechanics, 24, 122–134.

    Article  Google Scholar 

  53. Chi, Y. M., Jung, T. P., & Cauwenberghs, G. (2010). Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Reviews in Biomedical Engineering, 3, 106–119.

    Article  Google Scholar 

  54. Tseng, K. C., Lin, B. S., Liao, L. D., Wang, Y. T., & Wang, Y. L. (2014). Development of a wearable mobile electrocardiogram monitoring system by using novel dry foam electrodes. IEEE Systems Journal, 8, 900–906.

    Article  Google Scholar 

  55. Liao, L.-D., Wang, I.-J., Chen, S.-F., Chang, J.-Y., & Lin, C.-T. (2011). Design, fabrication and experimental validation of a novel dry-contact sensor for measuring electroencephalography signals without skin preparation. Sensors, 11, 5819.

    Article  Google Scholar 

  56. Lin, B. S., Chou, W., Wang, H. Y., Huang, Y. J., & Pan, J. S. (2013). Development of novel non-contact electrodes for mobile electrocardiogram monitoring system. IEEE Journal of Translational Engineering in Health and Medicine, 1, 1–8.

    Google Scholar 

  57. Ribeiro, D. M. D., Fu, L. S., Carlos, L. A. D., & Cunha, J. P. S. (2011). A novel dry active biosignal electrode based on an hybrid organic-inorganic interface material. IEEE Sensors Journal, 11, 2241–2245.

    Article  Google Scholar 

  58. Wang, Y., Pei, W., Guo, K., Gui, Q., Li, X., Chen, H., et al. (2011, October 19). Dry electrode for the measurement of biopotential signals. SCIENCE CHINA Information Sciences, 54, 2435.

    Article  Google Scholar 

  59. Sun, Y., & Yu, X. B. (2016). Capacitive biopotential measurement for electrophysiological signal acquisition: A review. IEEE Sensors Journal, 16, 2832–2853.

    Article  Google Scholar 

  60. Cömert, A., Honkala, M., & Hyttinen, J. (2013, April 08). Effect of pressure and padding on motion artifact of textile electrodes. Biomedical Engineering Online, 12, 26.

    Article  Google Scholar 

  61. Bandodkar, A. J., & Wang, J. (2014). Non-invasive wearable electrochemical sensors: A review. Trends in Biotechnology, 32, 363–371.

    Article  Google Scholar 

  62. Anna, G., Stefan, H., & Jörg, M. (2007). Novel dry electrodes for ECG monitoring. Physiological Measurement, 28, 1375.

    Article  Google Scholar 

  63. Lopez-Gordo, M. A., Sanchez-Morillo, D., & Valle, F. P. (2014, July 18). Dry EEG electrodes. Sensors (Basel, Switzerland), 14, 12847–12870.

    Article  Google Scholar 

  64. Chi, Y. M., & Cauwenberghs, G. (2009). Micropower non-contact EEG electrode with active common-mode noise suppression and input capacitance cancellation. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 4218–4221.

    Google Scholar 

  65. Chi, Y. M., Maier, C., & Cauwenberghs, G. (2011). Ultra-high input impedance, low noise integrated amplifier for noncontact biopotential sensing. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 1, 526–535.

    Article  Google Scholar 

  66. Griggs, D., Sharma, M., Naghibi, A., Wallin, C., Ho, V., Barbosa, K., et al. (2016). Design and development of continuous cuff-less blood pressure monitoring devices. In SENSORS, 2016 IEEE, 2016, pp. 1–3.

    Google Scholar 

  67. Schossow, D., Ritchie, P., Cao, H., Chiao, J.-C., Yang, J., & Xu, X. (2017). A novel design to power the micro-ECG sensor implanted in adult zebrafish. In Antennas and Propagation & USNC/URSI National Radio Science Meeting, 2017 IEEE International Symposium on, 2017, pp. 1681–1682.

    Google Scholar 

  68. Gruber, S., Le, T., Huerta, M., Wilson, K., Yang, J., Xu, X., et al.. (2018). Characterization of passive wireless electrocardiogram acquisition in Adult Zebrafish. In 2018 IEEE International Microwave Biomedical Conference (IMBioC), 2018, pp. 115–117.

    Google Scholar 

  69. Cao, H., Landge, V., Tata, U., Seo, Y.-S., Rao, S., Tang, S.-J., et al. (2012). An implantable, batteryless, and wireless capsule with integrated impedance and pH sensors for gastroesophageal reflux monitoring. IEEE Transactions on Biomedical Engineering, 59, 3131–3139.

    Article  Google Scholar 

  70. Gruber, S., Schossow, D., Lin, C.-y., Ho, C. H., Jeong, C., Lau, T. L., et al. (2017). Wireless power transfer for ECG monitoring in freely-swimming Zebrafish, presented at the IEEE Sensors, Glasgow, Scotland, 2017.

    Google Scholar 

  71. Brunger, J. M., Zutshi, A., Willard, V. P., Gersbach, C. A., & Guilak, F. (2017). CRISPR/Cas9 editing of murine induced pluripotent stem cells for engineering inflammation-resistant tissues. Arthritis & Rheumatology, 69, 1111–1121.

    Article  Google Scholar 

  72. Dimarco, J. P., & Philbrick, J. T. (1990). Use of ambulatory electrocardiographic (Holter) monitoring. Annals of Internal Medicine, 113, 53–68.

    Article  Google Scholar 

  73. Zheng, J. W., Zhang, Z. B., Wu, T. H., & Zhang, Y. (2007). A wearable mobihealth care system supporting real-time diagnosis and alarm. Medical & Biological Engineering & Computing, 45, 877–885.

    Article  Google Scholar 

  74. Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLoS Medicine, 13, e1001953.

    Article  Google Scholar 

  75. Milošević, M., Shrove, M. T., & Jovanov, E. (2011). “Applications of smartphones for ubiquitous health monitoring and wellbeing management,” JITA-Journal of Information Technology and Applications 1, 7–15.

    Google Scholar 

  76. Lee, Y.-G., Jeong, W. S., & Yoon, G. (2012). Smartphone-based mobile health monitoring. Telemedicine and e-Health, 18, 585–590.

    Article  Google Scholar 

  77. Cohrs, K. M., Dancy, J., Besko, D. P., Lohrman, L. L., & Miller, R. M. (2017). Combined strap and cradle for wearable medical monitor. ed: Google Patents, 2017.

    Google Scholar 

  78. Le, T., Huerta, M., Moravec, A., & Cao, H. (2018). Wireless passive monitoring of electrocardiogram in firefighters. In 2018 IEEE International Microwave Biomedical Conference (IMBioC), 2018, pp. 121–123.

    Google Scholar 

  79. Benharref, A., & Serhani, M. A. (2014). Novel cloud and SOA-based framework for E-Health monitoring using wireless biosensors. IEEE Journal of Biomedical and Health Informatics, 18, 46–55.

    Article  Google Scholar 

  80. Deo, R. C. (2015). Machine learning in medicine. Circulation, 132, 1920–1930.

    Article  Google Scholar 

  81. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. New England Journal of Medicine, 375, 1216–1219.

    Article  Google Scholar 

  82. (2018). NIH Data Sharing Repositories [Product, Program, and Project Descriptions]. Available: https://www.ncbi.nlm.nih.gov/pubmed/

  83. Géron, A. l. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: Concepts, tools, and techniques to build intelligent systems (1st ed.). Sebastopol: O’Reilly Media.

    Google Scholar 

  84. Waljee, A. K., & Higgins, P. D. R. (2010, 06/03/online). Machine learning in medicine: A primer for physicians. The American Journal Of Gastroenterology, 105, 1224.

    Article  Google Scholar 

  85. Szegedy, V. V. C., Ioffe, S., Shlens, J., & Wojna, Z. (2015). Rethinking the inception architecture for computer vision, 2015.

    Google Scholar 

  86. Pandit, D., Zhang, L., Aslam, N., Liu, C., Hossain, A., & Chattopadhyay, S. (2014) An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers. In The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), 2014, pp. 1–6.

    Google Scholar 

  87. Gupta, A., Thomas, B., Kumar, P., Kumar, S., & Kumar, Y. (2014). Neural network based indicative ECG classification, In 2014 5th International Conference – Confluence The Next Generation Information Technology Summit (Confluence), 2014, pp. 277–279.

    Google Scholar 

  88. Jun, H. J. P. T. J., Minh, N. H., Kim, D., & Kim, Y. H. (2016). Premature ventricular contraction beat detection with deep neural networks. In 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 859–864.

    Google Scholar 

  89. Halevy, A., Norvig, P., & Pereira, F. (2009). “The unreasonable effectiveness of data,” IEEE Intelligent Systems, 24, 8-12.

    Google Scholar 

  90. Park, J. Y., Noh, Y.-K., Choi, B. G., Rha, S.-W., & Kim, K. E. (2015). TCTAP A-010 a machine learning-based approach to prediction of acute coronary syndrome. Journal of the American College of Cardiology, 65, S6.

    Article  Google Scholar 

  91. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 15.

    Article  Google Scholar 

Download references

Acknowledgement

This work is financially supported by the NSF CAREER Award #1917105 (H.C.) and NIH R41 #OD024874 (H.C.).

Author information

Authors and Affiliations

  1. Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA, USA

    Tai Le, Isaac Clark, Joseph Fortunato & Hung Cao

  2. Electrical Engineering, University of Washington, Seattle, WA, USA

    Manuja Sharma

  3. Cardiology, Mayo Clinic, Rochester, MN, USA

    Xiaolei Xu

  4. Cardiology, University of California Los Angeles, Los Angeles, CA, USA

    Tzung K. Hsiai

  5. Biomedical Engineering, University of California Irvine, Irvine, CA, USA

    Hung Cao

Authors
  1. Tai Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isaac Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph Fortunato
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manuja Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaolei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tzung K. Hsiai
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hung Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hung Cao .

Editor information

Editors and Affiliations

  1. Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, USA

    Hung Cao

  2. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Todd Coleman

  3. Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA

    Tzung K. Hsiai

  4. California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA

    Ali Khademhosseini

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Le, T. et al. (2020). Electrocardiogram: Acquisition and Analysis for Biological Investigations and Health Monitoring. In: Cao, H., Coleman, T., Hsiai, T., Khademhosseini, A. (eds) Interfacing Bioelectronics and Biomedical Sensing. Springer, Cham. https://doi.org/10.1007/978-3-030-34467-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34467-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34466-5

  • Online ISBN: 978-3-030-34467-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation