An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats.

References

1. 1)
  - 24. Martis, R.J., Acharya, U.R., Mandana, K.M., et al: ‘Application of principal component analysis to ECG signals for automated diagnosis of cardiac health’, Expert Syst. Appl., 2012, 39, (14), pp. 11792–11800 (doi: 10.1016/j.eswa.2012.04.072).
2. 2)
  - 13. Christov, I., Jekova, I., Bortolan, G.: ‘Premature ventricular contraction classification by the Kth nearest-neighbours rule’, Physiol. Meas., 2005, 26, pp. 123–130 (doi: 10.1088/0967-3334/26/1/011).
3. 3)
  - 14. 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., 2004, 51, (7), pp. 1269–1273 (doi: 10.1109/TBME.2004.824131).
4. 4)
  - 22. Mark, R., Moody, G.: ‘The MIT-BIH arrhythmia database’. Available at http://physionet.org/physiobank/database/mitdb/.
5. 5)
  - 8. Bazi, Y., Hichri, H., Alajlan, N., et al: ‘Premature ventricular contraction arrhythmia detection and classification with Gaussian process and S transform’. Fifth Int. Conf. on Computational Intelligence, Communication Systems and Networks (CICSyN), 2013, pp. 36–41.
6. 6)
  - 21. Friesen, G.M., Jannett, T.C., Jadallah, M.A., et al: ‘A comparison of the noise sensitivity of nine QRS detection algorithms’, IEEE Trans. Biomed. Eng., 1990, 37, (1), pp. 85–98 (doi: 10.1109/10.43620).
7. 7)
  - 4. Sayadi, O., Shamsollahi, M.B., Clifford, G.D.: ‘Robust detection of premature ventricular contractions using a wave-based Bayesian framework’, IEEE Trans. Biomed. Eng., 2010, 57, (2), pp. 353–362 (doi: 10.1109/TBME.2009.2031243).
8. 8)
  - 3. Morris, F., Brady, W.J., Camm, J.: ‘ABC of clinical electrocardiography’, (BMJ Books, London, 2nd edn.).
9. 9)
  - 5. Lim, J.S.: ‘Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system’, IEEE Trans. Neural Netw., 2009, 20, pp. 522–527 (doi: 10.1109/TNN.2008.2012031).
10. 10)
  - 2. Naif Alajlan, Y., Bazi, F., Malek, M.S., et al: ‘Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods’, Signal Image Video Process., 2014, 8, pp. 931–942 (doi: 10.1007/s11760-012-0339-8).
11. 11)
  - 16. Manikandan, M.S., Samantaray, S.R., Kamwa, I.: ‘Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries’, IEEE Trans. Instrum. Meas., 2015, 64, (1), pp. 27–38 (doi: 10.1109/TIM.2014.2330493).
12. 12)
  - 19. Kligfield, P., et al: ‘Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology’, J. Am. Coll. Cardiol., 2007, 49, (10), pp. 1109–1127 (doi: 10.1016/j.jacc.2007.01.024).
13. 13)
  - 18. Yang, A.Y., Zhou, Z., Balasubramanian, A.G., et al: ‘Fast l1-minimization algorithms for robust face recognition’, IEEE Trans. Image Process., 2013, 22, (08), pp. 3234–46 (doi: 10.1109/TIP.2013.2262292).
14. 14)
  - 11. Ince, T., Kiranyaz, S., Gabbouj, M.: ‘Automated patient-specific classification of premature ventricular contractions’. Proc. of 30th Int. Conf. on IEEE EMBS, 2008, pp. 5474–5477.
15. 15)
  - 9. Khorrami, H., Moavenian, M.: ‘A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification’, Expert Syst. Appl., 2010, 37, pp. 5751–5757 (doi: 10.1016/j.eswa.2010.02.033).
16. 16)
  - 7. Adnane, M., Belouchrani, A.: ‘Premature ventricular contraction arrhythmia detection using wavelet coefficients’. Eighth Int. Workshop on Systems, Signal Processing and their Applications (WoSSPA), 2013, pp. 170–173.
17. 17)
  - 15. Manikandan, M.S., Ramkumar, B.: ‘Straightforward and robust QRS detection algorithm for wearable cardiac monitor’, Healthc. Technol. Lett., 2014, 1, (1), pp. 40–44 (doi: 10.1049/htl.2013.0019).
18. 18)
  - 10. Melgani, F., Bazi, Y.: ‘Classification of electrocardiogram signals with support vector machines and swarm particle optimization’, IEEE Trans. Inf. Technol. Biomed., 2008, 12, pp. 667–677 (doi: 10.1109/TITB.2008.923147).
19. 19)
  - 6. 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., 2006, 53, (12), pp. 2507–2515 (doi: 10.1109/TBME.2006.880879).
20. 20)
  - 12. Melgani, F., Bazi, Y.: ‘Detecting premature ventricular contractions in ECG signals with Gaussian processes’, Proc. Comput. Cardiol., 2008, 35, pp. 237–240.
21. 21)
  - 1. Chang, R.C.H., Lin, C.H., Wei, M.F., et al: ‘High-precision real-time premature ventricular contraction (PVC) detection system based on wavelet transform’, J. Signal Process. Syst., 2014, 77, (3), pp. 289–296 (doi: 10.1007/s11265-013-0823-6).
22. 22)
  - 20. Li, C., Zheng, C., Tai, C.: ‘Detection of ECG characteristic points using wavelet transforms’, IEEE Trans. Biomed. Eng., 1995, 42, pp. 21–28 (doi: 10.1109/10.362922).
23. 23)
  - 22. Aharon, M., Elad, M., Bruckstein, A.: ‘K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation’, IEEE Trans. Signal Process., 2006, 54, pp. 4311–4322 (doi: 10.1109/TSP.2006.881199).
24. 24)
  - 23. Martis, R.J., Acharya, U.R., Min, L.C.: ‘ECG beat classification using PCA, LDA, ICA and discrete wavelet transform’, Biomed. Signal Process. Control, 2013, 8, (5), pp. 437–448 (doi: 10.1016/j.bspc.2013.01.005).

Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

References

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