Pattern recognition and optimal parameter selection in premature ventricular contraction classification

Analyses of electrocardiographic pattern recognition parameters for premature ventricular contraction (PVC) and Normal (N) beat classification are presented. Twenty-six parameters are defined: 11×2 for the two ECG leads, 3 for vectorcardiogram (VCG) and width of the complex. Some of them include: amplitudes of maximal positive and negative peaks, area of the absolute values, area of positive and negative values, number of samples with 70% higher amplitude then that of the highest peak, amplitude and angle of the QRS vector in a VCG plane. They are measured for all N and PVC heart beats in the MIT-BIH arrhythmia database. The classification ability of each parameter is tested using discriminant analysis. Considering both leads 7 parameters with highest discriminant power for N and PVC are extracted and a specificity of 96.6% and a sensitivity of 90.5% are obtained. Taking into account relatively all parameters a specificity of 97.3% and a sensitivity of 93.3% are achieved.