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Time-Domain Analysis of the Electrocardiogram

  • Ioanna ChouvardaEmail author
  • Dimitris Filos
  • Nicos Maglaveras
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

The electrocardiogram (ECG) is an affordable and well-studied biosignal, which has a wide presence in clinical research and practice, especially as a frontline diagnostic tool that measures the evolution of the electrical activity of the heart, with characteristic morphologies for atrial and ventricular activity, depending also on the position of recording. Time domain analysis of ECG includes: (a) preprocessing for quality characterization and improvement, (b) recognition of ECG waves, (c) analysis of ECG waves morphology, durations, amplitudes, as well as distances among waves, (d) variability analysis, as regards evolution in time. Numerous application areas are based on these analysis building blocks, with arrhythmia detection, and risk analysis among them. Analysis of the ECG signals in the time domain has been a continuous research field, although later complemented by frequency and time-frequency analysis This chapter aims to provide an overview of ECG analysis methods along with the main clinical application areas. A description of the general characteristics and challenges of ECG in the time domain, is followed by summarizing the basic types of ECG analysis and reviewing analytics methods in the context of their clinical use. Finally, the current methods and future directions are discussed.

Keywords

ECG Time domain analysis 

References

  1. 1.
    AlGhatrif M, Lindsay J (2012) A brief review: history to understand fundamentals of electrocardiography. J Community Hosp Intern Med Perspect 2(1):1–5Google Scholar
  2. 2.
    Couderc JP (2012) The telemetric and holter ECG warehouse (THEW): the first three years of development and research. J Electrocardiol 45(6):677–683CrossRefGoogle Scholar
  3. 3.
    Badilini F (1998) The ISHNE holter standard output file format. Ann. Noninvasive Electrocardiol 3(3 I): 263–266CrossRefGoogle Scholar
  4. 4.
    Geselowitz DB (1964) Dipole theory in electrocardiography. Am J Cardiol 14(3):301–306CrossRefGoogle Scholar
  5. 5.
    Yang H, Bukkapatnam STS, Komanduri R (2012) Spatiotemporal representation of cardiac vectorcardiogram (VCG) signals. Biomed Eng (Online) 11(1):16CrossRefGoogle Scholar
  6. 6.
    Frank E (1956) An accurate, clinically practical system for spatial vectorcardiography. Circulation 13(5):737–749CrossRefGoogle Scholar
  7. 7.
    Ornato JP et al (2009) Body surface mapping vs 12-lead electrocardiography to detect ST-elevation myocardial infarction. Am J Emerg Med 27(7):779–784CrossRefGoogle Scholar
  8. 8.
    Malmivuo J, Plonsey R (2012) Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press, USAGoogle Scholar
  9. 9.
    Gima K, Rudy Y (2002) Ionic current basis of electrocardiographic waveforms: a model study. Circ Res 90(8):889–896CrossRefGoogle Scholar
  10. 10.
    Lin YD, Hu YH (2008) Power-line interference detection and suppression in ECG signal processing. IEEE Trans Biomed Eng 55(1):354–357CrossRefGoogle Scholar
  11. 11.
    Rahman MZU, Shaik RA, Reddy DVRK (2011) Cancellation of artifacts in ECG signals using block adaptive filtering techniques. In: Arabnia HR, Tran Q-N (eds) Software tools and algorithms for biological systems. Springer New York, New York, NY, pp 505–513Google Scholar
  12. 12.
    Luo S, Johnston P (2010) A review of electrocardiogram filtering. J Electrocardiol 486–496CrossRefGoogle Scholar
  13. 13.
    Milanesi M et al (2008) Independent component analysis applied to the removal of motion artifacts from electrocardiographic signals. Med Biol Eng Compu 46(3):251–261CrossRefGoogle Scholar
  14. 14.
    Kuzilek J et al (2014) Independent component analysis and decision trees for ECG holter recording de-noising. PLoS ONE, 9(6)CrossRefGoogle Scholar
  15. 15.
    Martis RJ, Acharya UR, Adeli H (2014) Current methods in electrocardiogram characterization. Comput Biol Med 48(1):133–149CrossRefGoogle Scholar
  16. 16.
    Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE transactions on bio-medical engineering 32(3):230–236CrossRefGoogle Scholar
  17. 17.
    Martínez A, Alcaraz R, Rieta JJ (2010) Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiol Meas 31(11):1467–1485CrossRefGoogle Scholar
  18. 18.
    Akhbari M et al (2016) ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations. Physiol Meas 37(2):203–226CrossRefGoogle Scholar
  19. 19.
    Almeida R et al (2009) Multilead ECG delineation using spatially projected leads from wavelet transform loops. IEEE Trans Biomed Eng 56(8):1996–2005CrossRefGoogle Scholar
  20. 20.
    Slocum J, Sahakian A, Swiryn S (1992) Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. J Electrocardiol 25(1):1–8CrossRefGoogle Scholar
  21. 21.
    Vaya C et al (2007) Convolutive blind source separation algorithms applied to the electrocardiogram of atrial fibrillation: Study of performance. IEEE Trans Biomed Eng 54(8):1530–1533CrossRefGoogle Scholar
  22. 22.
    Mateo J, Joaquín Rieta J (2013) Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation. Comput Biol Med 43(2):154–163CrossRefGoogle Scholar
  23. 23.
    Brembilla-Perrot B et al (2002) Absence of change of signal-averaged electrocardiogram identifies patients with ventricular arrhythmias who are non-responders to amiodarone. Int J Cardiol 83(1):47–55CrossRefGoogle Scholar
  24. 24.
    Kamath GS et al (2011) Value of the signal-averaged electrocardiogram in arrhythmogenic right ventricular cardiomyopathy/dysplasia. Heart Rhythm 8(2):256–262CrossRefGoogle Scholar
  25. 25.
    Tuzcu V et al (2000) P wave signal-averaged electrocardiogram as a new marker foratrial tachyarrhythmias in postoperative Fontan patients. J Am Coll Cardiol 36(2):602–607CrossRefGoogle Scholar
  26. 26.
    Verrier R et al (2010) T-wave alternans as a therapeutic marker for antiarrhythmic agents. J Cardiovasc Pharmacol 55(6):544–554CrossRefGoogle Scholar
  27. 27.
    Demidova MM et al (2014) Transient and rapid QRS-widening associated with a J-wave pattern predicts impending ventricular fibrillation in experimental myocardial infarction. Heart Rhythm 11(7):1195–1201CrossRefGoogle Scholar
  28. 28.
    Censi F et al (2016) P-wave variability and atrial fibrillation. Sci Rep 6:26799CrossRefGoogle Scholar
  29. 29.
    Cole CR et al (1999) Heart-rate recovery immediately after exercise as a predictor of mortality. N Engl J Med 341(18):1351–1357CrossRefGoogle Scholar
  30. 30.
    Task-force (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 17(3): 354–381Google Scholar
  31. 31.
    Goldberger AL et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefGoogle Scholar
  32. 32.
    Brateanu A (2015) Heart rate variability after myocardial infarction: what we know and what we still need to find out. Curr Med Res Opin 31(10):1855–1860CrossRefGoogle Scholar
  33. 33.
    Barbieri R et al (2005) A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. Am J Physiol Heart Circ Physiol 288(1):H424–H435MathSciNetCrossRefGoogle Scholar
  34. 34.
    Voss A et al (2009) Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans Ser A Math Phys Eng Sci 367(1887):277–296zbMATHCrossRefGoogle Scholar
  35. 35.
    Melillo P et al (2015) Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis. PLoS One 10(3)CrossRefGoogle Scholar
  36. 36.
    Michelucci A et al (2002) P wave assessment: state of the art update. Cardiac Electrophysiol Rev 6(3):215–220CrossRefGoogle Scholar
  37. 37.
    Dilaveris PE, Gialafos JE (2002) Future concepts in P wave morphological analyses. Card Electrophysiol Rev 6(3):221–224CrossRefGoogle Scholar
  38. 38.
    Ndrepepa G et al (2000) Relationship between surface electrocardiogram characteristics and endocardial activation sequence in patients with typical atrial flutter. Z Kardiol 89(6):527–537CrossRefGoogle Scholar
  39. 39.
    Poli S et al (2003) Prediction of atrial fibrillation from surface ECG: review of methods and algorithms. Annali dell’Istituto Superiore di Sanita 39(2):195–203Google Scholar
  40. 40.
    Hofmann M et al (1996) Analysis of the p wave in the signal-averaged electrocardiogram: normal values and reproducibility. Pacing Clin Electrophysiol 19(11):1928–1932CrossRefGoogle Scholar
  41. 41.
    Dilaveris PE, Gialafos JE (2001) P-wave dispersion: a novel predictor of paroxysmal atrial fibrillation. Ann Noninvasive Electrocardiol Off J Int Soc Holter Noninvasive Electrocardiology, Inc 6(2): 159–165CrossRefGoogle Scholar
  42. 42.
    Opolski G et al (1997) Detection of patients at risk for recurrence of atrial fibrillation after successful electrical cardioversion by signal-averaged p-wave ECG. Int J Cardiol 60(2):181–185CrossRefGoogle Scholar
  43. 43.
    Andrikopoulos GK et al (2000) Increased variance of P wave duration on the electrocardiogram distinguishes patients with idiopathic paroxysmal atrial fihrillation 23(July)Google Scholar
  44. 44.
    Passman R et al (2001) Predicting post—coronary bypass surgery atrial arrhythmias from the preoperative electrocardiogram. Am Heart J 142(5):806–810CrossRefGoogle Scholar
  45. 45.
    Martínez A, Alcaraz R, Rieta JJ (2012) Study on the P-wave feature time course as early predictors of paroxysmal atrial fibrillation. Physiol Meas 33(12):1959–1974CrossRefGoogle Scholar
  46. 46.
    Herreros A et al (2009) Analysis of changes in the beat-to-beat P-wave morphology using clustering techniques. Biomed Signal Process Control 4(4):309–316CrossRefGoogle Scholar
  47. 47.
    Huo Y et al (2015) Variability of P-wave morphology predicts the outcome of circumferential pulmonary vein isolation in patients with recurrent atrial fibrillation. J Electrocardiol 48(2):218–225CrossRefGoogle Scholar
  48. 48.
    Rangayyan RM (2002) Biomedical signal analysis: a case-study approach. Signals 552Google Scholar
  49. 49.
    Michaelides A et al (1993) Exercise-induced QRS prolongation in patients with coronary artery disease: a marker of myocardial ischemia. Am Heart J 126(6):1320–1325CrossRefGoogle Scholar
  50. 50.
    Takaki H et al (1999) Exercise-induced QRS prolongation in patients with mild coronary artery disease: computer analysis of the digitized multilead ECGs. J Electrocardiol 32 Suppl(0022–0736 (Print)): 206–211CrossRefGoogle Scholar
  51. 51.
    Birnie DH et al (2013) Impact of QRS morphology and duration on outcomes after cardiac resynchronization therapy results from the resynchronization defibrillation for ambulatory heart failure trial (RAFT). Circ Heart Fail 6(6):1190–1198CrossRefGoogle Scholar
  52. 52.
    Pietrasik G, Zareba W (2012) QRS fragmentation: diagnostic and prognostic significance. Cardiol J 19(2):114–121CrossRefGoogle Scholar
  53. 53.
    Lorgis L et al (2013) Prognostic value of fragmented QRS on a 12-lead ECG in patients with acute myocardial infarction. Heart Lung J Acute Crit Care 42(5):326–331CrossRefGoogle Scholar
  54. 54.
    Vassilikos VP et al (2014) QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study. J Electrocardiol 47(1):59–65CrossRefGoogle Scholar
  55. 55.
    Dodd KW, Elm KD, Smith SW (2016) Comparison of the QRS complex, ST-segment, and T-wave among patients with left bundle branch block with and without acute myocardial infarction. J Emerg Med 51(1):1–8CrossRefGoogle Scholar
  56. 56.
    Brady WJ et al (2001) Electrocardiographic ST-segment elevation: the diagnosis of acute myocardial infarction by morphologic analysis of the ST segment. Acad Emerg Med Off J Soc Acad Emerg Med 8(10):961–967CrossRefGoogle Scholar
  57. 57.
    Verrier RL, Nearing BD (2002) Modified moving average analysis of T-wave alternans to predict ventricular fibrillation with high accuracy. J Appl Physiol 92(2):541–549CrossRefGoogle Scholar
  58. 58.
    Baumert M et al (2016) QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electroph. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, p euv405Google Scholar
  59. 59.
    Schlegel TT et al (2010) Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction. BMC Cardiovasc Disord 10:28CrossRefGoogle Scholar
  60. 60.
    Platonov PG (2012) P-wave morphology: Underlying mechanisms and clinical implications. Ann Noninvasive Electrocardiol 17(3):161–169CrossRefGoogle Scholar
  61. 61.
    Stafford PJ, Turner I, Vincent R (1991) Quantitative analysis of signal averaged p wave in idiopathic paroxysmal atrial fibrillation. Am J Cardiol 751–755CrossRefGoogle Scholar
  62. 62.
    Jurkko R et al (2008) High-resolution signal-averaged analysis of atrial paroxysmal lone atrial fibrillation. Ann Noninvasive Electrocardiol 13(4):378–385CrossRefGoogle Scholar
  63. 63.
    Censi F et al (2007) P-wave morphology assessment by a Gaussian functions-based model in atrial fibrillation patients. IEEE Trans Biomed Eng 54(4):663–672CrossRefGoogle Scholar
  64. 64.
    Schotten U, Maesen B, Zeemering S (2012) The need for standardization of time-and frequency-domain analysis of body surface electrocardiograms for assessment of the atrial fibrillation substrate. Europace 14(8):1072–1075CrossRefGoogle Scholar
  65. 65.
    Christov I et al (2006) Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med Eng Phys 28(9):876–887CrossRefGoogle Scholar
  66. 66.
    Luz EJS et al (2016) ECG-based heartbeat classification for arrhythmia detection: a survey. Comput Methods Programs Biomed 127: 144–164CrossRefGoogle Scholar
  67. 67.
    Elhaj FA et al (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Programs Biomed 127:52–63CrossRefGoogle Scholar
  68. 68.
    Raj S, Ray KC, Shankar O (2016) Cardiac arrhythmia beat classification using DOST and PSO tuned SVM. Comput Methods Programs Biomed 136:163–177CrossRefGoogle Scholar
  69. 69.
    Steg PG et al (2012) ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J 33(20):2569–2619CrossRefGoogle Scholar
  70. 70.
    Reichlin T et al (2009) Early diagnosis of myocardial infarction with sensitive cardiac troponin assays. N Engl J Med 361:858–867CrossRefGoogle Scholar
  71. 71.
    Rubulis A et al (2004) T vector and loop characteristics in coronary artery disease and during acute ischemia. Heart Rhythm 1(3):317–325CrossRefGoogle Scholar
  72. 72.
    Barnhill JE et al (1989) Depolarization changes early in the course of myocardial infarction: significance of changes in the terminal portion of the QRS complex. J Am Coll Cardiol 14(1):143–149CrossRefGoogle Scholar
  73. 73.
    Surawicz B et al (1997) QRS changes during percutaneous transluminal coronary angioplasty and their possible mechanisms. J Am Coll Cardiol 30(2):452–458CrossRefGoogle Scholar
  74. 74.
    Romero D et al (2016) Ischemia detection from morphological QRS angle changes. Physiol Meas 37(7):1004–1023CrossRefGoogle Scholar
  75. 75.
    Pettersson J et al (2000) Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion. J Am Coll Cardiol 36(6):1827–1834CrossRefGoogle Scholar
  76. 76.
    van Campen CM, Visser FC, Visser CA (1996) The QRS score: a promising new exercise score for detecting coronary artery disease based on exercise-induced changes of Q-, R- and S-waves: a relationship with myocardial ischaemia. Eur Heart J 17(5):699–708CrossRefGoogle Scholar
  77. 77.
    Chouvarda J et al (2003) Wigner-Ville analysis and classification of electrocardiograms during thrombolysis. Med Biol Eng Comput 41(6):609–617CrossRefGoogle Scholar
  78. 78.
    Kalahasti V et al (2003) QRS duration and prediction of mortality in patients undergoing risk stratification for ventricular arrhythmias. Am J Cardiol 92(7)(United States PT-Journal Article LG-English): 798–803CrossRefGoogle Scholar
  79. 79.
    Strauss DG et al (2008) ECG quantification of myocardial scar in cardiomyopathy patients with or without conduction defects: correlation with cardiac magnetic resonance and arrhythmogenesis. Circ Arrhythm Electrophysiol 1(5):327–336CrossRefGoogle Scholar
  80. 80.
    Strauss DG et al (2013) Screening entire health system ECG databases to identify patients at increased risk of death. Circ Arrhythm Electrophysiol 6(6): 1156–1162Google Scholar
  81. 81.
    Gotsman I et al (2013) Usefulness of electrocardiographic frontal qrs-t angle to predict increased morbidity and mortality in patients with chronic heart failure. Am J Cardiol 111(10):1452–1459CrossRefGoogle Scholar
  82. 82.
    Chugh SS et al (2009) Determinants of prolonged QT interval and their contribution to sudden death risk in coronary artery disease: the Oregon sudden unexpected death study. Circulation 119(5):663–670CrossRefGoogle Scholar
  83. 83.
    Glancy JM et al (1995) QT dispersion and mortality after myocardial infarction. Lancet 345(0140–6736):945–948CrossRefGoogle Scholar
  84. 84.
    Patel C et al (2009) Is there a significant transmural gradient in repolarization time in the intact heart?: cellular basis of the T wave: a century of controversy. Circ Arrhythm Electrophysiol 2(1): 80–88Google Scholar
  85. 85.
    Erem B et al (2016) Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals. Phys Rev E Stat Nonlinear Soft Matter Phys 93(4)Google Scholar
  86. 86.
    Kennedy HL (2013) The evolution of ambulatory ECG monitoring. Prog Cardiovasc Dis 56(2):127–132CrossRefGoogle Scholar
  87. 87.
    Clifford GD et al (2011) Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments. In: 2011 Computing in cardiology (CinC), vol 1419, pp 285–288Google Scholar
  88. 88.
    Deserno TM, Marx N (2016) Computational electrocardiography: revisiting holter ECG monitoring. Methods Inf Med 55(4):305–311CrossRefGoogle Scholar
  89. 89.
    Choi E et al (2016) Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc JAMIA 292(3):344–350Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ioanna Chouvarda
    • 1
    Email author
  • Dimitris Filos
    • 1
  • Nicos Maglaveras
    • 1
  1. 1.Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, Medical School, Faculty of Health SciencesAristotle University of ThessalonikiThessalonikiGreece

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