Advertisement

Signal, Image and Video Processing

, Volume 8, Issue 1, pp 111–120 | Cite as

A low-complexity data-adaptive approach for premature ventricular contraction recognition

  • Peng Li
  • Chengyu Liu
  • Xinpei Wang
  • Dingchang Zheng
  • Yuanyang Li
  • Changchun LiuEmail author
Original Paper

Abstract

Premature ventricular contraction (PVC) may lead to life-threatening cardiac conditions. Real-time automated PVC recognition approaches provide clinicians the useful tools for timely diagnosis if dangerous conditions surface in their patients. Based on the morphological differences of the PVC beats in the ventricular depolarization phase (QRS complex) and repolarization phase (mainly T-wave), two beat-to-beat template-matching procedures were implemented to identify them. Both templates were obtained by a probability-based approach and hence were fully data-adaptive. A PVC recognizer was then established by analyzing the correlation coefficients from the two template-matching procedures. Our approach was trained on 22 ECG recordings from the MIT-BIH arrhythmia database (MIT-BIH-AR) and then tested on another 22 nonoverlapping recordings from the same database. The PVC recognition accuracy was 98.2 %, with the sensitivity and positive predictivity of 93.1 and 81.4 %, respectively. To evaluate its robustness against noise, our approach was applied again to the above testing set, but this time, the ECGs were not preprocessed. A comparable performance was still obtained. A good generalization capability was also confirmed by validating our approach on an independent St. Petersburg Institute of Cardiological Technics database. In addition, our performance was comparable with these published complex approaches. In conclusion, we have developed a low-complexity data-adaptive PVC recognition approach with good robustness against noise and generalization capability. Its performance is comparable to other state-of-the-art methods, demonstrating a good potential in real-time application.

Keywords

Electrocardiogram (ECG) Premature ventricular contraction (PVC) Low-complexity  Data-adaptive Template matching 

Notes

Acknowledgments

We would like to thank Mr. Jian Li and Miss. Xiuhua Ruan of Shandong University for their aids in the ECG data analysis and statistics and Dr. M. Llamedo of the University of Zaragoza for providing the useful MATLAB manuscript for manipulating recordings in the INCART database. We also thank the anonymous reviewers who gave valuable suggestion that has helped us to improve the quality of the manuscript. This work is supported by the Graduate Independent Innovation Foundation of Shandong University (GIIFSDU, yzc12082), the National Natural Science Foundation of China (61201049), the Excellent Young Scientist Awarded Foundation of Shandong Province (BS2012DX019) and the Independent Innovation Foundation of Shandong University (IIFSDU, 2011GN069).

References

  1. 1.
    Clifford, G.D., Moody, G.B.: Signal quality in cardiorespiratory monitoring. Physiol. Meas. 33(9), E01 (2012)Google Scholar
  2. 2.
    Nikita, K.S., Lin, J.C., Fotiadis, D.I., Arredondo, M.T.: Editorial: special issue on mobile and wireless technologies for healthcare delivery. IEEE Trans. Biomed. Eng. 59(11), 3083–3089 (2012)CrossRefGoogle Scholar
  3. 3.
    Iwasa, A., Hwa, M., Hassankhani, A., Liu, T., Narayan, S.M.: Abnormal heart rate turbulence predicts the initiation of ventricular arrhythmias. Pacing Clin. Electrophysiol. 28(11), 1189–1197 (2005)CrossRefGoogle Scholar
  4. 4.
    Liu, C.Y., Liu, C.C., Shao, P., Li, L.P., Sun, X., Wang, X.P., Liu, F.: Comparison of different threshold values \(r\) for approximate entropy: application to investigate the heart rate variability between heart failure and healthy control groups. Physiol. Meas. 32(2), 167–180 (2011)CrossRefGoogle Scholar
  5. 5.
    Liu, C.Y., Li, L.P., Zhao, L.N., Zheng, D.C., Li, P., Liu, C.C.: A combination method of improved impulse rejection filter and template matching for identification of anomalous intervals in electrocardiographic RR sequences. J. Med. Biol. Eng. 32(4), 245–250 (2012)CrossRefGoogle Scholar
  6. 6.
    Hu, Y.H., Palreddy, S., Tompkins, W.J.: A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 44(9), 891–900 (1997)CrossRefGoogle Scholar
  7. 7.
    Zhang, X., Zhu, Y., Thakor, N.V., Wang, Z.: Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans. Biomed. Eng. 46(5), 548–555 (1999)CrossRefGoogle Scholar
  8. 8.
    Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sornmo, L.: Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47(7), 838–848 (2000)CrossRefGoogle Scholar
  9. 9.
    de Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)CrossRefGoogle Scholar
  10. 10.
    Shyu, L.Y., Wu, Y.H., Hu, W.: Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG. IEEE Trans. Biomed. Eng. 51(7), 1269–1273 (2004)CrossRefGoogle Scholar
  11. 11.
    Sun, Y., Chan, K.L., Krishnan, S.M.: Life-threatening ventricular arrhythmia recognition by nonlinear descriptor. Biomed. Eng. Online 4(1), 6 (2005)CrossRefGoogle Scholar
  12. 12.
    de Chazal, P., Reilly, R.B.: A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53(12), 2535–2543 (2006)CrossRefGoogle Scholar
  13. 13.
    Inan, O.T., Giovangrandi, L., Kovacs, G.T.A.: Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans. Biomed. Eng. 53(12), 2507–2515 (2006)CrossRefGoogle Scholar
  14. 14.
    Lim, J.S.: Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system. IEEE Trans. Neural Netw. 20(3), 522–527 (2009)CrossRefGoogle Scholar
  15. 15.
    Homaeinezhad, M.R., Tavakkoli, E., Ghaffari, A.: Discrete wavelet-based fuzzy network architecture for ECG rhythm-type recognition: feature extraction and clustering-oriented tuning of fuzzy inference system. Int. J. Signal Proces. Image Process. and Pattern Recognit. 4(3), 107–130 (2011)Google Scholar
  16. 16.
    Llamedo, M., Martinez, J.P.: Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58(3), 616–625 (2011)CrossRefGoogle Scholar
  17. 17.
    Zhou, H., Hou, K.M., Ponsonnaille, J., Gineste, L., De Vaulx, C.: A real-time continuous cardiac arrhythmias detection system: RECAD. In: Proceedings of 27th Annual International Conference of the Engineering in Medicine and Biology Society. Shanghai, China, pp. 875–881, 17–18 Jan 2005 Google Scholar
  18. 18.
    Hu, S., Wei, H., Chen, Y., Tan, J.: A real-time cardiac arrhythmia classification system with wearable sensor networks. Sensors 12(9), 12844–12869 (2012)Google Scholar
  19. 19.
    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), E215–220 (2000)CrossRefGoogle Scholar
  20. 20.
    Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)CrossRefGoogle Scholar
  21. 21.
    Berger, R.D.: QT variability. J. Electrocardiol. 36(Suppl), 83–87 (2003)CrossRefGoogle Scholar
  22. 22.
    Li, P., Liu, C.Y., Liu, C.C., Sun, H., Yang, J., Ma, G.Q.: Higher order spectra for heart rate variability and QT interval variability analysis: a comparison between heart failure and normal control groups. In: Computing in Cardiology, Hangzhou, China, pp. 309–312, 18–21 Sep 2011 Google Scholar
  23. 23.
    Goh, K.W., Lavanya, J., Kim, Y., Tan, E.K., Soh, C.B.: A PDA-based ECG beat detector for home cardiac care. In: Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society. Shanghai, China, pp. 375–378, 17–18 Jan 2005Google Scholar
  24. 24.
    Tabakov, S., Iliev, I., Krasteva, V.: Online digital filter and QRS detector applicable in low resource ECG monitoring systems. Ann. Biomed. Eng. 36(11), 1805–1815 (2008)CrossRefGoogle Scholar
  25. 25.
    Li, P., Liu, C.C., Zhang, M., Che, W.B., Li, J.: A real-time QRS complex detection method. Acta Biophys. Sin. 27(3), 222–230 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Peng Li
    • 1
  • Chengyu Liu
    • 1
  • Xinpei Wang
    • 1
  • Dingchang Zheng
    • 2
  • Yuanyang Li
    • 3
  • Changchun Liu
    • 1
    Email author
  1. 1.School of Control Science and EngineeringShandong UniversityJinanPeople’s Republic of China
  2. 2.Medical Physics, Institute of Cellular MedicineFreeman Hospital, Newcastle UniversityNewcastle upon TyneUK
  3. 3.Department of Medical EngineeringProvincial Hospital Affiliated to Shandong UniversityJinanPeople’s Republic of China

Personalised recommendations