An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification

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.

[1]  F. J. Fernández,et al.  ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm , 2004, Comput. Biol. Medicine.

[2]  K. Egiazarian,et al.  Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. , 2006, Medical engineering & physics.

[3]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[4]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[5]  Dimitrios I. Fotiadis,et al.  An arrhythmia classification system based on the RR-interval signal , 2005, Artif. Intell. Medicine.

[6]  P. C. Cortez,et al.  A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique. , 2007, Medical engineering & physics.

[7]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[8]  Shubha Kadambe,et al.  Adaptive wavelets for signal classification and compression , 2006 .

[9]  Carsten Meyer,et al.  Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals , 2006, IEEE Transactions on Information Technology in Biomedicine.

[10]  Carlo Marchesi,et al.  Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis , 2006, Comput. Methods Programs Biomed..

[11]  J. Bouchaud.  An introduction to statistical finance , 2002 .

[12]  Gérard Dreyfus,et al.  Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators , 2007, Comput. Methods Programs Biomed..

[13]  Szi-Wen Chen,et al.  A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising , 2006, Comput. Methods Programs Biomed..

[14]  Elif Derya Übeyli.  ECG beats classification using multiclass support vector machines with error correcting output codes , 2007, Digit. Signal Process..

[15]  H. T. Nagle,et al.  A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.

[16]  Karsten Sternickel,et al.  Automatic pattern recognition in ECG time series , 2002, Comput. Methods Programs Biomed..

[17]  Stanislaw Osowski,et al.  Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study , 2002, Pattern Recognit..

[18]  Philippe Ravier,et al.  Redefining Performance Evaluation Tools for Real-Time QRS Complex Classification Systems , 2007, IEEE Transactions on Biomedical Engineering.

[19]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[20]  Szi-Wen Chen,et al.  Wavelet-based ECG compression by bit-field preserving and running length encoding , 2008, Comput. Methods Programs Biomed..

[21]  Willis J. Tompkins,et al.  Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database , 1986, IEEE Transactions on Biomedical Engineering.

[22]  Nurettin Acir.  Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm , 2005, Neural Computing & Applications.

[23]  H. H. So,et al.  Development of QRS detection method for real-time ambulatory cardiac monitor , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).