Research on premature ventricular contraction real-time detection based support vector machine

This paper proposes a support vector machine (SVM) for real-time detection of premature ventricular contraction (PVC) from normal beats and others. This includes a signal feature extraction module and a statistical pattern recognition module. In feature extraction, time, frequency and morphological features are extracted, here six features are selected and made up a feature vector for input the pattern identifier. After this, an SVM is used to recognize PVC from normal beats and others; this classifier is fit for the requirements of precision and real-time at the same time. Finally, by means of testing electrocardiogram (ECG) data which from MIT-BIH arrhythmia database, the correct rating is more than 97%. Through the comparison with other methods, this achieves favorable results both in real-time and accuracy requirement.

[1]  Sarabjeet Singh Mehta,et al.  Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram , 2008, Biomed. Signal Process. Control..

[2]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[3]  G Bortolan,et al.  Premature ventricular contraction classification by the Kth nearest-neighbours rule , 2005, Physiological measurement.

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

[5]  Philip de Chazal,et al.  A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[6]  G. Bortolan,et al.  Pattern recognition and optimal parameter selection in premature ventricular contraction classification , 2004, Computers in Cardiology, 2004.

[7]  Liang-Yu Shyu,et al.  Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[10]  Ataollah Ebrahimzadeh,et al.  Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features , 2010, Biomed. Signal Process. Control..

[11]  G. Bortolan,et al.  Comparison of four methods for premature ventricular contraction and normal beat clustering , 2005, Computers in Cardiology, 2005.

[12]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

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

[14]  G. Bortolan,et al.  Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks , 2004, Physiological measurement.

[15]  Madhuchhanda Mitra,et al.  A Rough-Set-Based Inference Engine for ECG Classification , 2006, IEEE Transactions on Instrumentation and Measurement.

[16]  Ataollah Ebrahimzadeh,et al.  Detection of premature ventricular contractions using MLP neural networks: A comparative study , 2010 .

[17]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .