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An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification
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 Title & Authors
An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification
Jiang, Bernard C.; Yang, Wen-Hung; Yang, Chi-Yu;
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Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.
RR Intervals;Forward-Backward Algorithm;Statistical Process Control;MIT-BIH Database;Arrhythmia Classification;
 Cited by
Acir, N. (2005), Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm, Neural Computing and Applications, 14(4), 299-309. crossref(new window)

Chan, H. L., Siao, Y. C., Chen, S. W., and Yu, S. F. (2008), Wavelet-based ECG compression by bitfield preserving and running length encoding, Computer Methods and Programs in Biomedicine, 90(1), 1-8. crossref(new window)

Chen, S. W., Chen, H. C., and Chan, H. L. (2006), A real-time QRS detection method based on movingaveraging incorporating with wavelet denoising, Computer Methods and Programs in Biomedicine, 82(3), 187-195. crossref(new window)

Christov, I., Gomez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A., and Egiazarian, K. (2006), Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification, Medical Engineering and Physics, 28(9), 876-887. crossref(new window)

De Chazal, P., O'Dwyer, M., and Reilly, R. B. (2004), Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Transactions on Biomedical Engineering, 51(7), 1196-1206. crossref(new window)

Dubois, R., Maison-Blanche, P., Quenet, B., and Dreyfus, G. (2007), Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators, Computer Methods and Programs in Biomedicine, 88(3), 217-233. crossref(new window)

Friesen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., and Nagle, H. T. (1990), A comparison of the noise sensitivity of nine QRS detection algorithms, IEEE Transactions on Biomedical Engineering, 37(1), 85-98. crossref(new window)

Hamilton, P. S. and Tompkins, W. J. (1986), Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, IEEE Transactions on Biomedical Engineering, 33(12), 1157-1165.

Jiang, B. C., Yang, W. H., and Chen, J. D. (2007), The detection of ECG R-wave based on the concept of slope and continuous runs, Proceedings of the 13th ISSAT International Conference on Reliability and Quality in Design, Seattle, WA.

Kadambe, S. and Srinivasan, P. (2006), Adaptive wavelets for signal classification and compression, AEU-International Journal of Electronics and Communications, 60(1), 45-55. crossref(new window)

Madeiro, J. P., Cortez, P. C., Oliveira, F. I., and Siqueira, R. S. (2007), A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique, Medical Engineering and Physics, 29(1), 26-37. crossref(new window)

Meyer, C., Gavela, J. F., and Harris, M. (2006), Combining algorithms in automatic detection of QRS complexes in ECG signals, IEEE Transactions on Information Technology in Biomedicine, 10(3), 468-475. crossref(new window)

MIT/BIH Arrhythmia Database (2007), MIT-BIH arrhythmia database, cited 2013 Nov 1, Available from:

Montgomery, D. C. (2005), Introduction to Statistical Quality Control, (5th ed.), Wiley, Hoboken, NJ.

Moody, G. B. and Mark, R. G. (2001), The impact of the MIT-BIH arrhythmia database, IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50. crossref(new window)

Osowski, S. and Nghia, D. D. (2002), Fourier and wavelet descriptors for shape recognition using neural networks: a comparative study, Pattern Recognition, 35(9), 1949-1957. crossref(new window)

Pan, J. and Tompkins, W. J. (1985), A real-time QRS detection algorithm, IEEE Transactions on Biomedical Engineering, 32(3), 230-236.

Paoletti, M. and Marchesi, C. (2006), Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis, Computer Methods and Programs in Biomedicine, 82(1), 20-30. crossref(new window)

Ravier, P., Leclerc, F., Dumez-Viou, C., and Lamarque, G. (2007), Redefining performance evaluation tools for real-time QRS complex classification systems, IEEE Transactions on Biomedical Engineering, 54(9), 1706-1710. crossref(new window)

Ros, E., Mota, S., Fernandez, F. J., Toro, F. J., and Bernier, J. L. (2004), ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm, Computers in Biology and Medicine, 34(8), 679-696. crossref(new window)

So, H. H. and Chan, K. L. (1997), Development of QRS detection method for real-time ambulatory cardiac monitor, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 289-292.

Sternickel, K. (2002), Automatic pattern recognition in ECG time series, Computer Methods and Programs in Biomedicine, 68(2), 109-115. crossref(new window)

Tsipouras, M. G., Fotiadis, D. I., and Sideris, D. (2005), An arrhythmia classification system based on the RR-interval signal, Artificial Intelligence in Medicine, 33(3), 237-250. crossref(new window)

Ubeyli, E. D. (2007), ECG beats classification using multiclass support vector machines with error correcting output codes, Digital Signal Processing, 17 (3), 675-684. crossref(new window)

Yen, S. Y. (2007), The ECG features detection and arrhythmia classification system, Master's thesis, Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, Taiwan.