Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks

Abstract

Detection and classification of ventricular complexes from a limited number of ECG leads is of considerable importance in critical care or operating room patient monitoring. Beat-to-beat detection allows the heart rhythm evolution to be followed and various types of arrhythmia to be recognized. A quantitative analysis is proposed of pattern recognition parameters for classification of normal QRS complexes and premature ventricular contractions (PVC). Twenty-six parameters have been defined: the width of the QRS complex, three vectorcardiogram parameters and 11 from two ECG leads. These parameters include: amplitudes of positive and negative peaks, area of positive and negative waves, various time-interval durations, amplitude and angle of the QRS vector, etc. They are measured for all QRS complexes annotated as 'normals' and 'PVCs' from the 48 ECG recordings of the MIT-BIH arrhythmia database. Neural networks (NN) are shown to be a useful instrument for the analysis of large quantities of parameters. Separate ranking of any parameter and homogeneous group ranking (amplitude, area, interval, slope and vector) were performed. From the two ECG leads, the first three ranked parameter groups for clustering of PVCs are amplitude, slope and interval, while for N clustering they are vector, amplitude and area. Considering the entire parameter set, we obtained N = 99.7% correct detection of normal QRS complexes and PVC = 98.5% of premature ventricular complexes. The study also shows that simultaneous analysis of two ECG channels yields better accuracy compared to using a single channel: the improvement is 0.1% in the classification of N beats and 4.5% for PVC beats.

Application of Computational Intelligence Techniques for Cardiovascular Diagnostics

“…Application of the wavelet transform, principal component analysis (PCA) and several types of artificial neural network structures to detect and classify different kinds of heart arrhythmias have also been reported (Silipo and Marchesi 1998); this study compared results of different neural network structures in order to find the best one for the classification of specific types of arrhythmias. A neural network classifier was used by (Christov and Bortolan 2004) to recognize premature ventricular contraction arrhythmia beats in an ECG signal database. A combination of neural network and discrete wavelet transform (DWT) has also been applied for detecting four types of heart arrhythmias .…”

Comparison of four methods for premature ventricular contraction and normal beat clustering

“…Since we focused only on the PVC classification, we followed the American Heart Association (AHA) records equivalent annotation [19], including some of the abnormal beats (left bundle branch block, right bundle branch block, aberrantly conducted beat, nodal premature beat, atrial premature beat, nodal or atrial premature beat, nodal escape beat, left or right bundle branch block, atrial ectopic beat and nodal ectopic beat) in the Normal group [3,5,6]. In addition, fusion premature ventricular contractions, ventricular flutter waves, ventricular escape beats, blocked APB, paced beats, missed beats and questionable beats were excluded from the study.…”

“…In addition numerous classification methods have been studied, and they include: adaptive signal processing for on-line estimation of non-stationary signals that present a recurrent behaviour [9][10][11][12][13], linear discriminants [4,5], neural networks [14,15,3,8], fuzzy adaptive resonance theory mapping [16], operation on vectors in the multidimensional space [6] and self-organized maps [17].…”

“…Several algorithms for the discrimination between normal beats (N) and premature ventricular contractions (PVC) have been proposed in literature, some of them using heart beat morphology parameters [1][2][3][4][5][6] or frequency-based parameters [7,8].…”

“…Some arrhythmias appear infrequently, and in order to capture them the clinicians use Holter devices. The use of specific algorithms for automatic analysis of ECG recordings may facilitate the analysis of the very long Holter ECG recordings.Several algorithms for the discrimination between normal beats (N) and premature ventricular contractions (PVC) have been proposed in literature, some of them using heart beat morphology parameters [1][2][3][4][5][6] or frequency-based parameters [7,8].In addition numerous classification methods have been studied, and they include: adaptive signal processing for on-line estimation of non-stationary signals that present a recurrent behaviour [9][10][11][12][13], linear discriminants [4,5], neural networks [14,15,3,8], fuzzy adaptive resonance theory mapping [16], operation on vectors in the multidimensional space [6] and self-organized maps [17].A particular aspect of the learning strategy is studied, paying attention to the organization of the classifiers' training set, and considering two main strategies: local learning set and global learning set [18,4,6]. In the first case the learning set is customized to the tested patient, while in the latter it is built from a large ECG database.…”