Assessing the severity of aortic valve stenosis by spectral analysis of cardiac murmurs (spectral vibrocardiography). Part I: Technical aspects

Abstract

Assessing the severity of aortic stenosis remains an important clinical problem. The turbulent pressure fluctuations generated by the confined jet down-stream of the stenotic valve produce vibrations in the aortic wall. These vibrations are transmitted through the chest to the skin surface, where they can be measured as systolic ejection murmurs. The purpose of the present study was to find the relationship between the severity of aortic valve stenosis and the frequency content of the precordial systolic murmurs, and to evaluate the transthoracic attenuation of murmurs and its variation from patient to patient. Twenty-four patients with clinical signs of aortic stenosis underwent cardiac catheterization to measure the peak transvalvular pressure difference. The mean energy density spectrum of the measured systolic precordial murmurs was calculated and the murmur energy ratio between 100-500 Hz and 20-500 Hz was correlated to the transvalvular pressure difference. The inter-individual variability of the transthoracic attenuation was evaluated by calculating the transthoracic transfer function from simultaneous measurements of precordial vibrations at the second right intercostal space and intravascular recordings of high frequency pressure fluctuations in the ascending aorta. The transvalvular pressure difference and the square root of the murmur energy ratio correlated well (r = 0.81, SEE = 27 mmHg). In the frequency range from 10-500 Hz the transthoracic transfer function could be modelled by a low-pass filter function with a low frequency attenuation of 36 +/- 7.7 dB (mean +/- SD), a corner frequency of 26 +/- 12 Hz and an attenuation slope of -29 +/- 7.9 dB/decade. Spectral analysis of systolic murmurs might be an attractive non-invasive addition to the array of techniques already in use for assessing the severity of aortic stenosis. It is a simple and cost effective technique, and requires less skill and time for data analysis than conventional methods.