Heartbeat Classification Using Normalized RR Intervals and Wavelet Features

This study developed an automatic classification system for identifying normal, atrial premature (AP) and premature ventricular contraction (PVC) heartbeats based on normalized RR intervals and wavelet morphological features. The proposed heartbeat classification system consists of signal pre processing, feature extraction, and linear discriminant classification (LDC). First, signal pre processing is applied to remove the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction includes the normalized RR intervals and the morphological features extracted by the wavelet analysis. Finally, the LDC method is applied to classify the heartbeats according to the extracted features. A total of 48 records obtained from the MIT-BIH arrhythmia database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The testing results show that the normalized RR intervals can enhance the sensitivity for identifying the AP heartbeats in the imbalanced and balanced testing datasets by of 21% and 22%, respectively, and there was an improvement of 18% in the positive prediction accuracy of the normal class in the balanced testing dataset in comparison with non-normalized RR intervals.

[1]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

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

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

[4]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[5]  Carsten Peterson,et al.  Clustering ECG Complexes Using Hermite Functions , 2000 .

[6]  A. Al-Fahoum,et al.  Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias , 1999, Medical & Biological Engineering & Computing.

[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]  M. Llamedo Soria,et al.  An ECG classification model based on multilead wavelet transform features , 2007, 2007 Computers in Cardiology.

[9]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[10]  Juan Pablo Martínez,et al.  Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria , 2011, IEEE Transactions on Biomedical Engineering.

[11]  Farid Melgani,et al.  Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization , 2008, IEEE Transactions on Information Technology in Biomedicine.

[12]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..

[13]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

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