Spatial Feature Extraction of Vectorcardiography via Minimum Volume Ellipsoid Enclosure in Classifying Left Ventricular Hypertrophy.

Abstract

The voltage criteria used to diagnose left ventricular hypertrophy (LVH) in the chest and limb leads are by no means absolute. In addition to QRS voltages, QRS axis and duration, and P wave characteristics, repolarization (STT) changes have been focused attention due to their representing left ventricular overload. Vectorcardiography (VCG) has been studied specifically on its repolarization abnormality. The present study aims to devise spatial feature extraction of VCG and assess it in the LVH classification task. A minimum volume ellipsoid enclosure was applied to six segments obtained from upstroke and downstroke of each P, QRS, and T loops of a single-beat VCG. For the evaluation, VCG and 12 lead ECG dataset along with LVH labels of 61 subjects were derived from public open data, PTB-XL. These classification performances were compared with the LVH diagnosis criteria in the standard 12 lead ECG. As a result, the Random Forest classifier trained by the proposed spatial VCG feature resulted in accuracy of 0.904 (95% confidence interval: 0.861-0.947) when the classbalanced dataset was evaluated, which slightly exceeded the feature of 12 lead ECG. The feature importance analysis provided the quantitative ranking of the spatial feature of VCG, which were practically similar to those of ECG in the LVH classification task. Since the VCG are spatially comparable with three-dimensional data of CT, MRI, or Echocardiography, VCG will shed light on the spatial behavior of electrical depolarization and repolarization abnormalities in cardiac diseases.