Cardia arrhythmia classification using neural networks
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This paper deals with the discrimination of premature ventricular contraction (PVC) arrhythmia using the fractal behavior of the power spectrum density of the QRS complexes. The linear interpolation of the QRS complex power spectrum density in Bode diagram in two different frequency intervals gives two straight lines with two different slopes. The scatter plot of one slope versus the other shows that there exists two distinct regions which represent the normal beats and the PVC beats. Therefore the PVC beats are classified using a self-organizing map fed by the two slopes of the QRS complex power spectrum. The MIT/BIH arrhythmia database is then used to evaluate the usefulness of the proposed method in the discrimination of the premature ventricular contraction (PVC) arrhythmia. The results have indicated that the method has achieved 82.71% of sensitivity and 88.06% of specificity over 46 records from the MIT-BIH arrhythmia database.