The mechanical processes within the cardiovascular system produce low-frequency vibratory and acoustic signals that can be recorded over the chest wall. Vibro-acoustic heart signals, including heart sounds (phonocardiogram), apical pulse (apexcardiogram) and arterial pulse (e.g. carotid pulse) carry valuable clinical information, but their use has been mostly limited to qualitative assessment by manual methods. The purpose of this work is to revisit automatic analysis of mechanical heart signals using modern signal processing algorithms, and to demonstrate the feasibility of using such algorithms to extract quantitative information that reliably represent the underlying physiological processes. A digital data acquisition system, able to simultaneously acquire multiple sensory modalities, was constructed in our lab. The system was used to acquire carotid pulse, apexcardiogram, phonocardiogram, ECG and echo-Doppler audio signals from healthy volunteers in rest and from cardiac patients during pharmacological stress test. Signal processing algorithms have been developed for automatic segmentation of the vibro-acoustic signals into distinct components and events, and extraction of temporal and morphological features on a beat-to-beat basis. Spectral analysis was used to reconstruct the profiles of the mitral and aortic blood flow from the Doppler audio signals. These profiles provided a 'gold-standard' reference estimation for the values of the physiological features.
A good agreement was observed between systolic and diastolic time intervals estimated automatically from the vibro-acoustic signals, and manually from the echo-Doppler reference (Figure 1). Strong beat-to-beat correlations were shown for the instantaneous filling time, as well as for ejection time and ejection amplitude.
Figure 5: The relationships between the CP signal and CW-Doppler of aortic blood flow (a) and between the ACG signal and TDI of the lateral wall (b). Beat-to-beat correlation between filling time derived from apexcardiogram and CW-Doppler of 109 heart cycles recorded from a healthy subject (c).
The results demonstrate the technological feasibility and the medical potential of using automatic analysis of vibro-acoustic heart signals for continuous non-invasive evaluation of the cardiovascular mechanical functionality. The proposed approach can set the grounds for wearable monitoring devices that will provide early alert about cardiac abnormalities.
