Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection.

BACKGROUND AND

OBJECTIVE

Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computational tools can provide essential assistance to specialists. This paper presents a new premature ventricular contraction recognition method based on a simplified set of features, extracted from geometric figures constructed over QRS complexes (Q, R and S waves).

METHODS

Initially, a preprocessing stage based on wavelet denoising electrocardiogram signal scaling is applied. Then, the signal is segmented taking into account the ventricular depolarization timing and a new set of geometrical features are extracted. In order to validate this approach, simulations encompassing eight different classifiers are presented. To select the best classifiers, a new methodology is proposed based on the Analytic Hierarchy Process.

RESULTS

The best results, achieved with an Artificial Immune System, were 98.4%, 91.1% and 98.7% for accuracy, sensitivity and specificity, respectively. When artificial examples were generated to balance the dataset, the recognition performance increased to 99.0%, 98.5% and 99.5%, employing the Support Vector Machine classifier.

CONCLUSIONS

The proposed approach is compared with some of latest references and results indicate its effectiveness as a method for recognizing premature ventricular contraction. Besides, the overall system presents low computation load.