Abstract
Premature ventricular contraction (PVC) is one of the most common arrhythmia diseases. The traditional diagnosis of PVC by visual inspection of PVC beats in electrocardiogram (ECG) is a time-consuming process. Hence, there has been an increasing interest in the study of automatic identification of PVC using ECGs in recent years. In this paper, a novel automatic PVC identification method is proposed. We first design a new approach to detect peak points of QRS complex. Then nine features are extracted from ECG according to the detected peak points, which are used to measure the morphological characteristics of PVC beats from different points of view. Finally, the key features are selected and fed into back propagation neural network (BPNN) to differentiate PVC ECGs from normal ECGs. Simulation results on the China Physiological Signal Challenge 2018 (CPSC2018) Database verify the feasibility and efficiency of the proposed method. The average accuracy attains 97.46%, as well as the average false detection rate and omission ratio are 3.41% and 1.37% respectively, which implies that the proposed method does a good job in identifying PVC automatically.
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References
Ebrahimzadeh, A., Khazaee, A.: Detection of premature ventricular contractions using MLP neural networks: a comparative study. Measurement 43, 103–112 (2010)
Bazi, Y., Hichri, H., Alajlan, N., Ammour, N.: Premature ventricular contraction arrhythmia detection and classification with Gaussian process and S transform. In: 2013 Fifth International Conference on Computational Intelligence, pp. 36–41. IEEE (2013)
Bhardwaj, P., Choudhary, R.R., Dayama, R.: Analysis and classification of cardiac arrhythmia using ECG signals. Int. J. Comput. Appl. 38, 37–40 (2012)
Chang, C.H., Lin, C.H., Wei, M.F.: High-precision real-time premature ventricular contraction (PVC) detection system based on wavelet transform. J. Signal Process. Syst. 77, 289–296 (2014)
Cuesta, P., Lado, M.J., Vila, X.A.: Detection of premature ventricular contractions using the RR-interval signal: a simple algorithm for mobile devices. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 22, 651–656 (2014)
Deutsch, E., Svehlikova, J., Tysler, M.: Effect of elimination of noisy ECG leads on the noninvasive localization of the focus of premature ventricular complexes. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds.) World Congress on Medical Physics and Biomedical Engineering 2018, vol. 68. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-9035-6_14. ISBN 978-981-10-9034-9
Jung, Y., Kim, H.: Detection of PVC by using a wavelet-based statistical ECG monitoring procedure. Biomed. Signal Process. Control. 36, 176–182 (2017)
Lee, J., Mcmanus, D., Chon, K.: Atrial fibrillation detection using time-varying coherence function and shannon entropy. IEEE Eng. Med. Biol. Soc. 104, 4685–4688 (2011)
Liu, C.Y., Li, P., Zhang, Y.T., Zhang, Y., Liu, C.C., Wei, S.S.: A construction method of personalized ECG template and its application in premature ventricular contraction recognition for ECG mobile phones. Expert. Syst. Appl. 24, 85–92 (2012)
Liu, X.L., Du, H.M., Wang, G.L.: Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network. Comput. Methods Progams Biomed. 122, 47–55 (2015)
Liu, Y., Huang, Y., Wang, J.: Detecting premature ventricular contraction in children with deep learning. J. Shanghai Jiaotong Univ. (Sci.) 23, 66–73 (2018)
Mabrouki, R., Khaddoumi, B., Sayadi, M.: Atrial fibrillation detection on electrocardiogram. In: International Conference on Advanced Technologies for Signal and Image Processing, vol. 34, pp. 268–272 (2016)
Pachauri, A., Bhuyan, M.: Wavelet and energy based approach for PVC detection. In: 2009 International Conference on Emerging Trends in Electronic and Photonic Devices and Systems, pp. 257–261. IEEE (2010)
Sun, Y., Chan, K.L., Krishnan, S.M.: Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc. Disord. 5, 28 (2005)
Tsipouras, M.G., Fotiadis, D.I., Sideris, D.: An arrhythmia classification system based on the RR-interval signal. Artif. Intell. Med. 33, 237–250 (2005)
Wei, J.Y., Wang, D., Sun, Y.N., Zhang, R.: A novelfusion feature extraction method for atrial fibrillation detection. J. Northwest Univ. 49, 19–26 (2019)
Winkens, R.A.G., Höppener, P.F., Kragten, J.A.: Are premature ventricular contractions always harmless? Eur. J. Gen. Pract. 20, 134–138 (2014)
Zhou, F.Y., Jin, L.P., Dong, J.: Premature ventricular contraction detection combining deep neural networks and rules inference. Artif. Intell. Med. 79, 42–51 (2017)
Acknowledgement
This work was supported by the Innovative Talents Promotion Plan of Shaanxi Province under Grant 2018TD-016.
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Chen, H., Bai, J., Mao, L., Wei, J., Song, J., Zhang, R. (2019). Automatic Identification of Premature Ventricular Contraction Using ECGs. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_14
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DOI: https://doi.org/10.1007/978-3-030-32962-4_14
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