Heart Rate Turbulence Denoising Using Support Vector Machines

Abstract

Heart Rate Turbulence (HRT) is the transient acceleration and subsequent deceleration of the heart rate after a premature ventricular complex (PVC), and it has been shown to be a strong risk stratification criterion in patients with cardiac disease. In order to reduce the noise level of the HRT signal, conventional measurements of HRT use a patient-averaged template of post-PVC tachograms (PPT), hence providing with long-term HRT indices. We hypothesize that the reduction of the noise level at each isolated PPT, using signal processing techniques, will allow to estimate short-term HRT indices. Accordingly, its application could be extended to patients with reduced number of available PPT. In this paper, several HRT denoising procedures are proposed and tested, with special attention to Support Vector Machine (SVM) estimation, as this is a robust algorithm that allows us to deal with few available time samples in the PPT. Pacing stimulated HRT during electrophysiological study are used as a low noise gold-standard. Measurements in a 24 hour-Holter patient database reveal a significant reduction in the the bias and in the variance of HRT measurements. We conclude that SVM denoising yields short-term HRT measurements and improves the signal to noise level of long-term HRT measurements.