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Artificial Intelligence-Enabled ECG Big Data Mining for Pervasive Heart Health Monitoring

  • Qingxue ZhangEmail author
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Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

The ECG signal is a gold standard physiological signal to reflect heart health and has been studied for many decades. It can not only be leveraged to generate real-time emergency alarms before a heart attack but also be mined in a long-term manner for risk pattern discovery. ECG signal has specific characteristics in multiple domain, such as the temporal, frequency, statistical, and phase domains, each of which can reveal some interesting medical hints related to the mechanical and electrical behaviors of the heart. Traditionally, ECG signals are acquired in clinics or hospitals, where very high signal quality can be guaranteed. However, along with the advance of wearable computers and mobile computing platforms, there are many emerging ECG-based heart disease management possibilities, i.e., possibly, we can monitor the ECG signal in our daily lives and effectively track our heart health without going to medical facilities. However, it is very challenging to deal with motion artifacts during people’s physical activates. Many researchers have proposed a large number of ECG signal processing algorithms and studied a bunch of potential applications. We have introduced artificial intelligence into wearable ECG-based heart rate monitoring during severe human activities, which greatly outperform previous studies. We have also studied novel ECG applications, which can capture single-arm-ECG for 24-hour heart disease monitoring. The goal in this work is to eliminate the uncomfortableness and inconvenience induced by traditional ECG configurations, i.e., the 12-lead ECG placement methods. Especially, previously, people put the ECG electrodes on the chest, which requires a chest strap to fix the electrodes. Leveraging advanced signal sensing and artificial intelligence algorithms, we have successfully demonstrated the potential of this highly wearable arm-worn heart rate monitor. This chapter will systematically introduce the current advancement of ECG signal processing algorithms and applications, including previous works and our research progress. The readers from both academia and industry can benefit from the chapter by understanding the advancement, challenges, and future opportunities from this chapter.

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References

  1. 1.
    Al-Jawad, A., Adame, M.R., Romanovas, M., Hobert, M., Maetzler, W., Traechtler, M., et al.: Using multi-dimensional dynamic time warping for tug test instrumentation with inertial sensors. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 212–218. IEEE (2012)Google Scholar
  2. 2.
    Alemdar, H., Tunca, C., Ersoy, C.: Daily life behaviour monitoring for health assessment using machine learning: bridging the gap between domains. Pers. Ubiquitous Comput. 19(2), 303–315 (2015)CrossRefGoogle Scholar
  3. 3.
    AlGhatrif, M., Lindsay, J.: A brief review: history to understand fundamentals of electrocardiography. J. Community Hosp. Intern. Med. Perspect. 2(1), 14383 (2012)CrossRefGoogle Scholar
  4. 4.
    Awal, M.A., Mostafa, S.S., Ahmad, M., Rashid, M.A.: An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern. Biomed. Eng. 34(4), 238–249 (2014)CrossRefGoogle Scholar
  5. 5.
    Baig, M.M., Gholamhosseini, H.: Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 9898 (2013)CrossRefGoogle Scholar
  6. 6.
    Baig, M.M., Gholamhosseini, H.: Smart health monitoring systems: an overview of design and modeling. J. Med. Syst. 37(2), 1–14 (2013)CrossRefGoogle Scholar
  7. 7.
    Birjandtalab, J., Zhang, Q., Jafari, R.: A case study on minimum energy operation for dynamic time warping signal processing in wearable computers. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 415–420. IEEE (2015)Google Scholar
  8. 8.
    Chai, R., Naik, G.R., Ling, S.H., Nguyen, H.T.: Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems. Biomed. Eng. Online 16(1), 5 (2017)Google Scholar
  9. 9.
    Clark, S.L., Hamilton, E.F., Garite, T.J., Timmins, A., Warrick, P.A., Smith, S., et al.: The limits of electronic fetal heart rate monitoring in the prevention of neonatal metabolic acidemia. Am. J. Obstet. Gynecol. 216(2), 163, e1–163, e6 (2017)CrossRefGoogle Scholar
  10. 10.
    Clifford, G., McSharry, P., Tarassenko, L.: Characterizing artefact in the normal human 24-hour RR time series to aid identification and artificial replication of circadian variations in human beat to beat heart rate using a simple threshold. In: Computers in Cardiology, 2002, pp. 129–132. IEEE (2002)Google Scholar
  11. 11.
    da Silva, M.J.: Characterization of QRS complex in ecg signals applying wavelet transform. In: 2015 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE), pp. 86–89. IEEE (2015)Google Scholar
  12. 12.
    Das, M.K., Saha, C., El Masry, H., Peng, J., Dandamudi, G., Mahenthiran, J., et al.: Fragmented QRS on a 12-lead ECG: a predictor of mortality and cardiac events in patients with coronary artery disease. Heart Rhythm 4(11), 1385–1392 (2007)CrossRefGoogle Scholar
  13. 13.
    Das, M.K., Suradi, H., Maskoun, W., Michael, M.A., Shen, C., Peng, J., et al.: Fragmented wide QRS on a 12-lead ECG: a sign of myocardial scar and poor prognosis. Circ.: Arrhythmia Electrophysiol. 1(4), 258–268 (2008)Google Scholar
  14. 14.
    Di Brina, C., Niels, R., Overvelde, A., Levi, G., Hulstijn, W.: Dynamic time warping: a new method in the study of poor handwriting. Hum. Mov. Sci. 27(2), 242–255 (2008)CrossRefGoogle Scholar
  15. 15.
    Freyermuth, F., Rau, F., Kokunai, Y., Linke, T., Sellier, C., Nakamori, M., et al.: Splicing misregulation of SCN5A contributes to cardiac-conduction delay and heart arrhythmia in myotonic dystrophy. Nat. Commun. 7, 11067 (2016)Google Scholar
  16. 16.
    Gribok, A.V., Chen, X., Reifman, J.: A robust method to estimate instantaneous heart rate from noisy electrocardiogram waveforms. Ann. Biomed. Eng. 39(2), 824–834 (2011)CrossRefGoogle Scholar
  17. 17.
    Guo, Y., Naik, G.R., Huang, S., Abraham, A., Nguyen, H.T.: Nonlinear multiscale maximal Lyapunov exponent for accurate myoelectric signal classification. Appl. Soft Comput. 36, 633–640 (2015)CrossRefGoogle Scholar
  18. 18.
    Hall, J.E.: Guyton and Hall Textbook of Medical Physiology E-BOOK. Elsevier Health Sciences (2015)Google Scholar
  19. 19.
    He, H., Wang, Z., Tan, Y.: Noise reduction of ECG signals through genetic optimized wavelet threshold filtering. In: 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 1–6. IEEE (2015)Google Scholar
  20. 20.
    Hijazi, S., Page, A., Kantarci, B., Soyata, T.: Machine learning in cardiac health monitoring and decision support. Computer 49(11), 38–48 (2016)CrossRefGoogle Scholar
  21. 21.
    Kern, M.J., Sorajja, P., Lim, M.J.: Cardiac Catheterization Handbook E-Book. Elsevier Health Sciences (2015)Google Scholar
  22. 22.
    Khan, E., Al Hossain, F., Uddin, S.Z., Alam, S.K., Hasan, M.K.: A robust heart rate monitoring scheme using photoplethysmographic signals corrupted by intense motion artifacts. IEEE Trans. Biomed. Eng. 63(3), 550–562 (2016)CrossRefGoogle Scholar
  23. 23.
    Kim, H., Kim, S., Van Helleputte, N., Berset, T., Geng, D., Romero, I., et al.: Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system. In: 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 160–163. IEEE (2012)Google Scholar
  24. 24.
    Kotas, M.: Application of dynamic time warping to ECG processing. In: Proceedings of the International Conference on MIT, Poland, IX, pp. 169–175 (2006)Google Scholar
  25. 25.
    Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)CrossRefGoogle Scholar
  26. 26.
    Li, Q., Mark, R.G., Clifford, G.D.: Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol. Meas. 29(1), 15 (2008)CrossRefGoogle Scholar
  27. 27.
    Li, Q., Rajagopalan, C., Clifford, G.D.: A machine learning approach to multi-level ECG signal quality classification. Comput. Methods Programs Biomed. 117(3), 435–447 (2014)CrossRefGoogle Scholar
  28. 28.
    Liu, S.-H.: Motion artifact reduction in electrocardiogram using adaptive filter. J. Med. Biol. Eng. 31(1), 67–72 (2011)CrossRefGoogle Scholar
  29. 29.
    Ly, Q.T., Handojoseno, A.A., Gilat, M., Chai, R., Martens, K.A.E., Georgiades, M., et al.: Detection of turning freeze in Parkinson’s disease based on S-transform decomposition of EEG signals. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3044–3047. IEEE (2017)Google Scholar
  30. 30.
    Lymberis, A.: Smart wearable systems for personalised health management: current R&D and future challenges In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2003, pp. 3716–3719. IEEE (2003)Google Scholar
  31. 31.
    Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A.P., et al.: Machine learning for internet of things data analysis: a survey. Digit. Commun. Netw. 4(3), 161–175 (2018)CrossRefGoogle Scholar
  32. 32.
    Maron, B.J., Friedman, R.A., Kligfield, P., Levine, B.D., Viskin, S., Chaitman, B.R., et al.: Assessment of the 12-Lead ECG as a screening test for detection of cardiovascular disease in healthy general populations of young people (12–25 years of age). Circulation 130(15), 1303–1334 (2014)CrossRefGoogle Scholar
  33. 33.
    Marraffa, J., Holland, M., Sullivan, R., Morgan, B., Oakes, J., Wiegand, T., et al.: Cardiac conduction disturbance after loperamide abuse. Clin. Toxicol. 52(9), 952–957 (2014)CrossRefGoogle Scholar
  34. 34.
    Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning: An Artificial Intelligence Approach. Springer Science & Business Media (2013)Google Scholar
  35. 35.
    Mukhopadhyay, S., Biswas, S., Roy, A.B., Dey, N.: Wavelet based qrs complex detection of ECG signal. Int. J. Eng. Res. Appl. 2(3), 2361–2365 (2012)Google Scholar
  36. 36.
    Myers, C., Rabiner, L.R., Rosenberg, A.E.: Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Trans. Acoust., Speech Signal Process. 28(6), 623–635 (1980)zbMATHCrossRefGoogle Scholar
  37. 37.
    Naik, G.R., Baker, K.G., Nguyen, H.T.: Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: evaluation using SDICA. IEEE J. Biomed. Health Inform. 19(5), 1689–1696 (2015)CrossRefGoogle Scholar
  38. 38.
    Naik, G.R., Selvan, S.E., Gobbo, M., Acharyya, A., Nguyen, H.T.: Principle component analysis applied to surface electromyography: a comprehensive review. IEEE Access (2016)Google Scholar
  39. 39.
    Perlman, O., Katz, A., Weissman, N., Amit, G., Zigel, Y.: Atrial electrical activity detection using linear combination of 12-lead ECG signals. IEEE Trans. Biomed. Eng. 61(4), 1034–1043 (2014)CrossRefGoogle Scholar
  40. 40.
    Phinyomark, A., Limsakul, C., Phukpattaranont, P.: A novel feature extraction for robust EMG pattern recognition. arXiv preprint: arXiv:0912.3973 (2009)Google Scholar
  41. 41.
    Pramanik, M.I., Lau, R.Y., Demirkan, H., Azad, M.A.K.: Smart health: big data enabled health paradigm within smart cities. Expert. Syst. Appl. 87, 370–383 (2017)CrossRefGoogle Scholar
  42. 42.
    Sayadi, O., & Shamsollahi, M.B.: ECG denoising with adaptive bionic wavelet transform. In: Conference proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, pp. 6597–6600 (2005)Google Scholar
  43. 43.
    Sayadi, O., Shamsollahi, M.B.: ECG denoising and compression using a modified extended Kalman filter structure. IEEE Trans. Biomed. Eng. 55(9), 2240–2248 (2008)CrossRefGoogle Scholar
  44. 44.
    Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press (2014)Google Scholar
  45. 45.
    Solanas, A., Patsakis, C., Conti, M., Vlachos, I.S., Ramos, V., Falcone, F., et al.: Smart health: a context-aware health paradigm within smart cities. IEEE Commun. Mag. 52(8), 74–81 (2014)CrossRefGoogle Scholar
  46. 46.
    Song, J., Shan, T., Zhu, S., Chiu, Y.: A motion-artifact tracking and compensation technique for dry-contact EEG monitoring system In: 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1–4. IEEE (2014)Google Scholar
  47. 47.
    Sprint, G., Cook, D., Fritz, R., Schmitter-Edgecombe, M.: Detecting health and behavior change by analyzing smart home sensor data. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–3. IEEE (2016)Google Scholar
  48. 48.
    Sra, S., Nowozin, S., Wright, S.J.: Optimization for Machine Learning. Mit Press (2012)Google Scholar
  49. 49.
    Sun, B., Zhang, Z.: Photoplethysmography-based heart rate monitoring using asymmetric least squares spectrum subtraction and bayesian decision theory. IEEE Sens. J. 15(12), 7161–7168 (2015)CrossRefGoogle Scholar
  50. 50.
    Tomašić, I., Trobec, R.: Electrocardiographic systems with reduced numbers of leads—synthesis of the 12-Lead ECG. IEEE Rev. Biomed. Eng. 7, 126–142 (2014)CrossRefGoogle Scholar
  51. 51.
    Tsouri, G.R., Ostertag, M.H.: Patient-specific 12-lead ECG reconstruction from sparse electrodes using independent component analysis. IEEE J. Biomed. Health Inform. 18(2), 476–482 (2014)CrossRefGoogle Scholar
  52. 52.
    van Andel, J., Ungureanu, C., Aarts, R., Leijten, F., Arends, J.: Using photoplethysmography in heart rate monitoring of patients with epilepsy. Epilepsy & Behav. 45, 142–145 (2015)Google Scholar
  53. 53.
    Varon, C., Caicedo, A., Testelmans, D., Buyse, B., Huffel, S.V.: A novel algorithm for the automatic detection of sleep apnea from single-lead ECG (2015)Google Scholar
  54. 54.
    Veeraraghavan, R., Gourdie, R.G., Poelzing, S.: Mechanisms of cardiac conduction: a history of revisions. Am. J. Physiol.-Hear. Circ. Physiol. 306(5), H619–H627 (2014)CrossRefGoogle Scholar
  55. 55.
    Vullings, R., De Vries, B., Bergmans, J.W.: An adaptive Kalman filter for ECG signal enhancement. IEEE Trans. Biomed. Eng. 58(4), 1094–1103 (2011)CrossRefGoogle Scholar
  56. 56.
    Vyas, N.: Biomedical Signal Processing. Pinnacle Technology (2011)Google Scholar
  57. 57.
    Wang, B.R., Park, J.-Y., Chung, K., Choi, I.Y.: Influential factors of smart health users according to usage experience and intention to use. Wireless Pers. Commun. 79(4), 2671–2683 (2014)CrossRefGoogle Scholar
  58. 58.
    Warren, S., Craft, R.L., Bosma, B.: Designing smart health care technology into the home of the future. In: Workshops on Future Medical Devices: Home Care Technologies for the 21st Century, p. 667 (1999)Google Scholar
  59. 59.
    WHO: The 10 leading causes of death in the world. http://www.who.int/mediacentre/factsheets/fs310/en/ (2014)
  60. 60.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2016)Google Scholar
  61. 61.
    Yadav, T., Mehra, R.: Denoising and SNR improvement of ECG signals using wavelet based techniques. In: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), pp. 678–682. IEEE (2016)Google Scholar
  62. 62.
    Zhang, Q.: Deep learning of electrocardiography dynamics for biometric human identification in era of IoT. In: 2018 IEEE 9th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). IEEE (in press) (2018)Google Scholar
  63. 63.
    Zhang, Q., Zeng, X., Hu, W., Zhou, D.: A machine learning-empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with ear-ECG/PPG. IEEE Access 5, 10547–10561 (2017)CrossRefGoogle Scholar
  64. 64.
    Zhang, Q., Zhou, D., Zeng, X.: A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals. Physiol. Meas. 37(11), 1945 (2016)CrossRefGoogle Scholar
  65. 65.
    Zhang, Q., Zhou, D., Zeng, X.: Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals. Biomed. Eng. Online 16(1), 23 (2017)CrossRefGoogle Scholar
  66. 66.
    Zhang, Q., Zhou, D., Zeng, X.: A novel framework for motion-tolerant instantaneous heart rate estimation by phase-domain multi-view dynamic time warping. IEEE Trans. Biomed. Eng. 64(11), 2562–2574 (2017)CrossRefGoogle Scholar
  67. 67.
    Zhang, Z., Pi, Z., Liu, B.:. TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans. Biomed. Eng. 62(2), 522–531 (2015)CrossRefGoogle Scholar
  68. 68.
    Zipes, D.P., Libby, P., Bonow, R.O., Mann, D.L., Tomaselli, G.F.: Braunwald’s Heart Disease E-Book: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences (2018)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA

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