부산대학교 도서관

Heart Rate Turbulence (HRT) is a powerful risk stratification criterion in patients with cardiac disorders. Several physiological factors affect HRT, e.g., previous cardiac cycle (CC), coupling interval (CI), and compensatory pause (CP). However, classical HRT measurements often use an average of the available individual tachograms that might blur relevant physiological relationships. We hypothesized that filtering individual tachograms, by using robust signal processing techniques, would allow to compute local HRT measurements and to relate them with their local physiological conditions. In this paper, a denoising procedure based in support vector machine (SVM) estimation was used. HRT indices, Turbulence Slope (TS) and Tubulence Onset (TO), were computed in filtered individual tachograms by using 24-h Holter recordings from Congestive Heart Failure (CHF) patients. The relationship between local TS and TO parameters and their physiological conditions (CC, CI and CP) was quantified by linear regression. SVM filtering procedure might allow taking into account the local physiological conditions, which modulate the HRT response, and give a way to quantify this modulation. This approach could complete the current HRT assessment methods, and yield a clearer physiological interpretation of the HRT parameters.