Skip to main content
SpringerLink
Log in

Model-Based Atrial Fibrillation Detection

  • Chapter
  • First Online:
ECG Signal Processing, Classification and Interpretation

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia, leading to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure disease, one of the main causes of hospitalization. Thus, detection of AF becomes decisive in the diagnosis and prevention of cardiac threats, with particular interest in the context of pHealth solutions.This chapter presents a real-time AF detection scheme, developed within the MyHeart project, a pHealth project financed by the European Commission. The proposed strategy is based on a computational intelligence approach, combining expert knowledge and neural networks. In particular, it makes use of the three principal physiological characteristics of AF, applied by cardiologists in their daily reasoning: P wave absence/presence, heart rate irregularity, and atrial activity analysis. This knowledge-based approach has the advantage of increasing interpretability of the results to the medical community, while improving detection robustness.The clinical validation of the strategy is performed using public databases (MIT-BIH Arrhythmia and QT databases from Physionet) as well as the ECG data acquired with the MyHeart vest during the MyHeart trial.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  • Alan, G., Hylek, E., Phillips, K., Chang, Y., Henault, L., Selby, J., Singer, D.: Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the nticoagulation and risk factors in atrial fibrillation (ATRIA) study. JAMA. 285, 2370–2375 (2001)

    Article  Google Scholar 

  • Al-Nashash, H.: Cardiac arrhythmia classification using neural networks. Technol. Health Care. 8(6), 363–372 (2000)

    Google Scholar 

  • Cerutti, S., Mainardi, L.T., Porta, A., Bianchi, A.: Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes. IEEE Comput. Cardiol. 24, 77–80 (1997)

    Google Scholar 

  • Chikh, M., Gbelgacem, N., Reguig, F.: The use of artificial neural networks to detect PVC beats. Lab. de Génie Biomédical. Dép. d’électronique, Univ. Abou Bekr Belkaïd (2003)

    Google Scholar 

  • Chikh, M., Belgacem, N., Chikh, A., Reguig, F.: The use of artificial neural network to detect the premature ventricular contraction (PVC) beats. Electron. J. Tech. Acoust. 2, 1–14 (2004)

    Google Scholar 

  • Christov, I., Bortolan, G.: Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks. Physiol. Meas. 25, 1281–1290 (2004)

    Article  Google Scholar 

  • Christov, I., Jekova, I., Bortolan, G.: Premature ventricular contraction classification by the Kth nearest-neighbours rule. Physiol. Meas. 26, 123–130 (2005)

    Article  Google Scholar 

  • Cleland, J., Louis, A., Rigby, A., Janssens, U., Balk, A.: Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death. J. Am. Coll. Cardiol. 45, 1654–1664 (2005)

    Article  Google Scholar 

  • Couceiro, R., Carvalho, P., Henriques, J., Antunes, M.: On the detection of premature ventricular contractions. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008:1087–1091 (2008)

    Google Scholar 

  • Fuster, V., Rydén, L., Asinger, R., Cannom, D., Crijns, H., Frye, R., Halperin, J., Kay, G., Klein, E., Lévy, S., MCNamara, R., Prysowsky, E., Wann, L., Wyse, D.: ACC/AHA/ESC guidelines for the management of patients with atrial fibrillation. J. Am. Coll. Cardiol. 38(4), 1231–1265, 765 (2001)

    Google Scholar 

  • Habetha, J.: The myheart project – fighting cardiovascular diseases by prevention and early diagnosis. Conf. Proc. IEEE Eng. Med. Biol. Soc. 6746–6749 (2006)

    Google Scholar 

  • Ham, F., Han, S.: Classification of cardiac arrhythmias using Fuzzy ARTMAP. IEEE Trans. Biomed. Eng. 43(4), 425–430 (1996)

    Article  Google Scholar 

  • Jalife, J.: Experimental and clinical AF mechanisms: bridging the divide. J. Intervent. Card. Electrophysiol. 9, 85–92 (2003)

    Article  Google Scholar 

  • Jekova, I., Bortolan, G., Christov, I.: Pattern recognition and optimal parameter selection in premature ventricular contraction classification. IEEE Comput. Cardiol. 31, 357–360 (2004)

    Article  Google Scholar 

  • Levy, A., Camm, J., Saksena, S.: International consensus on nomenclature and classification of atrial fibrillation: a collaborative of the working group an arrhythmias and the work group of cardiac pacing of the European society of cardiology and the north American society of pain and electrophysiology. Cardiovasc. Electrophysiol. 14, 443–445 (2003)

    Article  Google Scholar 

  • Lévy, S., Breithardt, G., Campbell, R., Camm, A., Daubert, J., Allessie, M., Aliot, E., Capucci, A., Cosio, F., Crijns, H., Jordaens, L., Hauer, R., Lombardi, F., Lüderitz, B.: Atrial fibrillation: current knowledge and recommendations for management. Eur. Heart J. 19(9), 1294–1320 (1998)

    Article  Google Scholar 

  • Millet, J., Perez, M., Joseph, G., Mocholi, A., Chorro, J.: Previous identification of QRS onset and offset is not essential for classifying QRS complexes in a single lead. IEEE Comput. Cardiol. 24, 299–302 (1997)

    Google Scholar 

  • Moe, G., Abildskov, J.: Observations on the ventricular dysrhythmia associated with atrial fibrillation in the dog heart. Circ. Res. 4, 447–460 (1964)

    Google Scholar 

  • Moody, B., Mark, R.: A new method for detecting atrial fibrillation using RR intervals. IEEE Comput. Cardiol. 10, 227–230 (1983)

    Google Scholar 

  • Moraes, J., Seixas, M., Vilani, F., Costa, E.: A real time QRS complex classification method using Mahalanobis distance. IEEE Comput. Cardiol. 29, 201–204 (2002)

    Google Scholar 

  • Omer, T., Giovangrandi, L., Kovacs, G.: Robust neural-network-based classification of premature ventricular contraction using wavelet transform and timing interval features. IEEE Trans. Biomed. Eng. 53(12), 2507–2515 (2006)

    Article  Google Scholar 

  • Reiter, H., Maglaveras, N.: HeartCycle: compliance and effectiveness in HF and CAD closed-loop management. Conf. Proc. IEEE Eng. Med. Biol. Soc. 299–302 (2009)

    Google Scholar 

  • Ribeiro, B., Marques, A., Henriques, J.: Choosing real-time predictors for ventricular arrhythmia detection. Int. J. Pattern Recog. Artifi. Intell. 21(8), 1–15 (2007)

    Google Scholar 

  • Sanchez, C., Millet, J., Rieta, J., Castells, F., Ródenas, J., Ruiz-Granell, R., Ruiz, V.: Packet wavelet decomposition: an approach for atrial activity extraction. IEEE Comput. Cardiol. 29, 33–36 (2002)

    Google Scholar 

  • Senhadji, L., Wang, F., Hernandez, A., Carrault, G.: Wavelets extrema representation for QRS-T cancellation and P wave detection. IEEE Comput. Cardiol. 29, 37–40 (2002)

    Google Scholar 

  • Sherman, D.: Pharmacological rate versus rhythm control. Stroek. 38, 615 (2007)

    Google Scholar 

  • Shkurovich, S., Sahakian, A., Swiryn, S.: Detection of atrial activity from high-voltage leads of implantable ventricular fibrillators, using a cancellation technique. IEEE Trans. Biomed. Eng. 45(2), 229–234 (1998)

    Article  Google Scholar 

  • Sun, Y., Chan, K., Krishnan, S.: Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc. Disord. 5, 28 (2005)

    Article  Google Scholar 

  • Tateno, K., Glass, L.: A method for detection of atrial fibrillation using RR intervals. IEEE Comput. Cardiol. 27, 391–394 (2000)

    Google Scholar 

  • Waldo, A.: Mechanisms of atrial fibrillation. J. Cardiovasc. Electrophysiol. 14(12), 267–274 (2003)

    Article  Google Scholar 

  • Wang, J., Yeo, C., Aguire, A.: The design and evaluation of a new multi-lead arrhythmia monitoring algorithm. IEEE Comput. Cardiol. 26, 675–678 (1999)

    Google Scholar 

  • Wieben, O., Afonso, V., Tompkinks, W.: Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system. Med. Biol. Eng. Comput. 37(1), 560–565 (1999)

    Article  Google Scholar 

  • Wolf, P., Abbott, R., Kannel, W.: Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 22, 983–988 (1991)

    Article  Google Scholar 

Download references

Acknowledgments

This project was partially financed by the IST FP6 MyHeart project, IST-2002-507816 supported by the European Union.

Author information

Authors and Affiliations

  1. Center for Informatics and Systems, University of Coimbra, 3030-290, Coimbra, Portugal

    Paulo de Carvalho, Jorge Henriques & Ricardo Couceiro

  2. Cardio-thoracic Surgery Center, University Hospital of Coimbra, Coimbra, Portugal

    Matthew Harris

  3. Faculty of Medicine, University of Coimbra, Coimbra, Portugal

    Matthew Harris

  4. Philips Research Laboratories, Aachen, Germany

    Manuel Antunes & Joerg Habetha

Authors
  1. Paulo de Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Henriques
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricardo Couceiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manuel Antunes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joerg Habetha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo de Carvalho .

Editor information

Editors and Affiliations

  1. Institute of Medical Technology and Equi, Roosevelta 118, Zabrze, 41-800, Poland

    Adam Gacek

  2. Dept. Electrical & Computer Engineering, University of Alberta, 116 Street 9107, Edmonton, T6G 2V4, Alberta, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

de Carvalho, P., Henriques, J., Couceiro, R., Harris, M., Antunes, M., Habetha, J. (2012). Model-Based Atrial Fibrillation Detection. In: Gacek, A., Pedrycz, W. (eds) ECG Signal Processing, Classification and Interpretation. Springer, London. https://doi.org/10.1007/978-0-85729-868-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-868-3_5

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-867-6

  • Online ISBN: 978-0-85729-868-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation