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A classification scheme for ventricular arrhythmias using wavelets analysis

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Abstract

Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7 % and then the separation between OVF and DVF with an accuracy of 80.0 %. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.

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References

  1. Abdullah Arafat M, Sieed J, Kamrul Hasan M (2009) Detection of ventricular fibrillation using empirical mode decomposition and bayes decision theory. Comput Biol Med 39(11):1051–1057

    Article  PubMed  Google Scholar 

  2. Addison P (2005) Wavelet transforms and the ecg: a review. Physiol Meas 26:R155

    Article  PubMed  Google Scholar 

  3. Akritas A, Malaschonok G, Vigklas P (2006) The svd-fundamental theorem of linear algebra. Nonlinear Anal 11(2):123–136

    Google Scholar 

  4. Angrisani L, Daponte P, D’Apuzzo M, Testa A (1998) Ameasurement method based on the wavelet transform for power quality analysis. IEEE Trans Power Del 13(4):990–998

    Article  Google Scholar 

  5. Asl B, Setarehdan S, Mohebbi M (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif Intell Med 44(1):51–64

    Article  PubMed  Google Scholar 

  6. Balasundaram K, Masse S, Nair K, Farid T, Nanthakumar K, Umapathy K (2011) Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options. In: Engineering in Medicine and Biology Society, 2011. IEMBS’11. 33rd Annual international conference of the IEEE, vol 1. IEEE, New York, pp 969–972

  7. Barquero-Pe′rez O′, Rojo-Alvarez J, Caaman˜ o A, Goya-Esteban R, Everss E, Alonso-Atienza F, Sa′nchez-Munoz J, Garc′ıa-Alberola A (2010) Fundamental frequency and regularity of cardiac electrograms with fourier organization analysis. Biomed Eng IEEE Trans 57(9):2168–2177

  8. Casey M (1998) Auditory group theory with applications to statistical basis methods for structured audio. Ph.D. thesis, Massachusetts Institute of Technology

  9. Cheng K, Dosdall D, Li L, Rogers J, Ideker R, Huang J (2012) Evolution of activation patterns during long-duration ventricular fibrillation in pigs. Am J Physiol Heart Circ Physiol 302(4):H992–H1002

    Article  PubMed  CAS  Google Scholar 

  10. Ciaccio E, Coromilas J, Wit A, Garan H (2011) Onset dynamics of ventricular tachyarrhythmias as measured by dominant frequency. Heart Rhythm 8(4):615–623

    Google Scholar 

  11. Clayton R, Murray A, Campbell R (1994) Changes in the surface electrocardiogram during the onset of spontaneous ventricular fibrillation in man. Eur Heart J 15(2):184

    PubMed  CAS  Google Scholar 

  12. Foomany FH, Umapathy K, Krishnan S, Masse S, Farid T, Nair K, Dorian P, Nanthakumar K (2010) Wavelet-based markers of ventricular fibrillation in optimizing human cardiac resuscitation. In: Conference proceedings: IEEE Engineering in Medicine and Biology Society, pp 2001–2004

  13. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, New York

  14. Fukuoka Y, Oostendorp T, Armoundas A (2009) Method for guiding the ablation catheter to the ablation site: a simulation and experimental study. Med Biol Eng Comput 47(3):267–278

    Article  PubMed  Google Scholar 

  15. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215

    Google Scholar 

  16. Hassanpour H, Mesbah M, Boashash B (2004) Time-frequency feature extraction of newborn eeg seizure using svd-based techniques. EURASIP J Appl Signal Process 2004:2544–2554

    Article  Google Scholar 

  17. Huang J, Rogers J, Killingsworth C, Singh K, Smith W, Ideker R (2004) Evolution of activation patterns during long-duration ventricular fibrillation in dogs. Am J Physiol Heart Circul Physiol 286(3):H1193

    Article  CAS  Google Scholar 

  18. Khadra L, Al-Fahoum A, Al-Nashash H (1997) Detection of life-threatening cardiac arrhythmias using the wavelet transformation. Med Biol Eng Comput 35(6):626–632

    Article  PubMed  CAS  Google Scholar 

  19. Krasteva V, Jekova I (2005) Assessment of ecg frequency and morphology parameters for automatic classification of life-threatening cardiac arrhythmias. Physiol Meas 26:707

    Article  PubMed  Google Scholar 

  20. Lerma C, Wessel N, Schirdewan A, Kurths J, Glass L (2008) Ventricular arrhythmias and changes in heart rate preceding ventricular tachycardia in patients with an implantable cardioverter defibrillator. Med Biol Eng Comput 46(7):715–727

    Article  PubMed  Google Scholar 

  21. Li H, Han W, Hu C, Meng M (2009) Detecting ventricular fibrillation by fast algorithm of dynamic sample entropy. In: Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference. IEEE, New York, pp 1105–1110

  22. Mallat S (1999) A wavelet tour of signal processing. Academic Press, New York

  23. Masse’ S, Downar E, Chauhan V, Sevaptsidis E, Nanthakumar K (2007) Ventricular fibrillation in myopathic human hearts: mechanistic insights from in vivo global endocardial and epicardial mapping. Am J Physiol Heart Circ Physiol 292(6):H2589

    Google Scholar 

  24. Mishkin J, Saxonhouse S, Woo G, Burkart T, Miles W, Conti J, Schofield R, Sears S, Aranda J Jr (2009) Appropriate evaluation and treatment of heart failure patients after implantable cardioverter-defibrillator discharge: time to go beyond the initial shock. J Am Coll Cardiol 54(22):1993–2000

    Article  PubMed  Google Scholar 

  25. Namarvar H, Shahidi A (2004) Cardiac arrhythmias predictive detection methods with wavelet-svd analysis and support vector machines. In: Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th Annual international conference of the IEEE, vol 1. IEEE, New York, pp 365–368

  26. Nanthakumar K, Walcott G, Melnick S, Rogers J, Kay M, Smith W, Ideker R, Holman W (2004) Epicardial organization of human ventricular fibrillation. Heart Rhythm 1(1):14–23

    Article  PubMed  Google Scholar 

  27. Narayan SM, Krummen DE, Shivkumar K, Clopton P, Rappel WJ, Miller JM (2012) Treatment of atrial fibrillation by the ablation of localized sources. J Am Coll Cardiol 60(7):628–636

    Article  PubMed  Google Scholar 

  28. Omar M, Solouma N, Kadah Y (2006) Morphological characterization of ecg signal abnormalities: a new approach. In: Proceedings of Cairo international biomedical engineering conference, pp 1–5

  29. Ozkurt N, Savaci F (2005) Determination of wavelet ridges of nonstationary signals by singular value decomposition. Circuits Syst II Express Briefs IEEE Trans 52(8):480–485. doi:10.1109/TCSII.2005.849041

    Article  Google Scholar 

  30. Potse M, Essebag V (2009) Guidance for catheter ablation of ventricular arrhythmia. Med Biol Eng Comput 47(3):241–243

    Article  PubMed  Google Scholar 

  31. Povinelli RJ, Roberts FM, Ropella KM, Johnson MT (2002) Are nonlinear ventricular arrhythmia characteristics lost, as signal duration decreases? In: Proceedings. Computers in Cardiology, Memphis, TN, pp 221–224

  32. Roberts F, Povinelli R, Ropella K (2001) Identification of ecg arrhythmias using phase space reconstruction. Principles of data mining and knowledge discovery, pp 411–423

  33. Rocha T, Paredes S, de Carvalho P, Henriques J, Antunes M (2008) Phase space reconstruction approach for ventricular arrhythmias characterization. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual international conference of the IEEE. IEEE, New York, pp 5470–5473

  34. Ropella K, Baerman J, Sahakian A, Swiryn S (1990) Differentiation of ventricular tachyarrhythmias. Circulation 82(6):2035–2043

    Article  PubMed  CAS  Google Scholar 

  35. Samie F, Berenfeld O, Anumonwo J, Mironov S, Udassi S, Beaumont J, Taffet S, Pertsov A, Jalife J (2001) Rectification of the background potassium current: a determinant of rotor dynamics in ventricular fibrillation. Circ Res 89(12):1216

    Article  PubMed  CAS  Google Scholar 

  36. Samie F, Jalife J (2001) Mechanisms underlying ventricular tachycardia and its transition to ventricular fibrillation in the structurally normal heart. Cardiovasc Res 50(2):242

    Article  PubMed  CAS  Google Scholar 

  37. Sierra G, de Jesu’s Go’mez M, Le Guyader P, Trelles F, Cardinal R, Savard P, Nadeau R (1998) Discrimination between monomorphic and polymorphic ventricular tachycardia using cycle length variability measured by wavelet transform analysis. J Electrocardiol 31(3):245–255

  38. SPSS Inc (1990) SPSS advanced statistics user’s guide. In: User manual, SPSS Inc., Chicago, IL

  39. Strang G (2003) Introduction to linear algebra. Wellesley Cambridge Press, Wellesley

  40. Strohmenger H, Lindner K, Brown C (1997) Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans. Chest 111(3):584

    Article  PubMed  CAS  Google Scholar 

  41. Ten Tusscher K, Mourad A, Nash M, Clayton R, Bradley C, Paterson D, Hren R, Hayward M, Panfilov A, Taggart P (2009) Organization of ventricular fibrillation in the human heart: experiments and models. Exp Physiol 94(5):553

    Article  PubMed  Google Scholar 

  42. Thomas S, Thiagalingam A, Wallace E, Kovoor P, Ross D (2005) Organization of myocardial activation during ventricular fibrillation after myocardial infarction. Circulation 112(2):157–163

    Article  PubMed  Google Scholar 

  43. Tsipouras M, Fotiadis D, Sideris D (2002) Arrhythmia classification using the RR-interval duration signal. In: Computers in cardiology. IEEE, New York, pp 485–488

  44. Umapathy K, Krishnan S, Masse S, Hu X, Dorian P, Nanthakumar K (2009) Optimizing cardiac resuscitation outcomes using wavelet analysis. In: International conference of the IEEE engineering in medicine and biology society, vol 1, pp 6761–6764

  45. Venkatachalam V, Aravena J (1999) Nonstationary signal classification using pseudo power signatures: the matrix svd approach. Circuits and systems II: analog and digital signal processing. IEEE Trans 46(12):1497–1505. doi:10.1109/82.809535

    Google Scholar 

  46. Wall M, Rechtsteiner A, Rocha L (2003) Singular value decomposition and principal component analysis. In: A practical approach to microarray data analysis, pp 91–109

  47. Wathen M, DeGroot P, Sweeney M, Stark A, Otterness M, Adkisson W, Canby R, Khalighi K, Machado C, Rubenstein D et al (2004) Prospective randomized multicenter trial of empirical antitachycardia pacing versus shocks for spontaneous rapid ventricular tachycardia in patients with implantable cardioverter-defibrillators. Circulation 110(17):2591–2596

    Article  PubMed  Google Scholar 

  48. Wathen M, Sweeney M, DeGroot P, Stark A, Koehler J, Chisner M, Machado C, Adkisson W (2001) Shock reduction using antitachycardia pacing for spontaneous rapid ventricular tachycardia in patients with coronary artery disease. Circulation 104(7):796–801

    Article  PubMed  CAS  Google Scholar 

  49. Watson J, Addison P, Clegg G, Holzer M, Sterz F, Robertson C (2000) A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation. Resuscitation 43(2):121–127

    Article  PubMed  CAS  Google Scholar 

  50. Wilkoff B, Kuhlkamp V, Volosin K, Ellenbogen K, Waldecker B, Kacet S, Gillberg J, DeSouza C (2001) Critical analysis of dual-chamber implantable cardioverter-defibrillator arrhythmia detection: results and technical considerations. Circulation 103(3):381

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

The authors would like to thank the Hull family cardiac fibrillation management laboratory, Toronto General Hospital, and in particular Dr K. Nanthakumar for his expert opinion on this work. The authors also thankfully acknowledge Natural Sciences & Engineering Research Council of Canada (Grant to Dr. K. Umapathy) for supporting this work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada

    K. Balasundaram & K. Umapathy

  2. The Hull Family Cardiac Fibrillation Management Lab, Toronto General Hospital Toronto, Toronto, Canada

    S. Masse & K. Nair

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  1. K. Balasundaram
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  2. S. Masse
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  3. K. Nair
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  4. K. Umapathy
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Correspondence to K. Umapathy.

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Balasundaram, K., Masse, S., Nair, K. et al. A classification scheme for ventricular arrhythmias using wavelets analysis. Med Biol Eng Comput 51, 153–164 (2013). https://doi.org/10.1007/s11517-012-0980-y

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  • DOI: https://doi.org/10.1007/s11517-012-0980-y

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