Non-contact quantification of aortic stenosis and mitral regurgitation using carotid waveforms from skin displacements

Objective. Early diagnosis of heart problems is essential for improving patient prognosis. Approach. We created a non-contact imaging system that calculates the vessel-induced deformation of the skin to estimate the carotid artery pressure displacement waveforms. We present a clinical study of the system in patients (n = 27) with no underlying condition, aortic stenosis (AS), or mitral regurgitation (MR). Main results. Displacement waveforms were compared to aortic catheter pressures in the same patients. The morphologies of the pressure and displacement waveforms were found to be similar, and pulse wave analysis metrics, such as our modified reflection indices (RI) and waveform duration proportions, showed no significant differences. Compared with the control group, AS patients displayed a greater proportion of time to peak (p = 0.026 and p = 0.047 for catheter and displacement, respectively), whereas augmentation index (AIx) was greater for the displacement waveform only (p = 0.030). The modified RI for MR (p = 0.047 and p = 0.004 for catheter and displacement, respectively) was lower than in the controls. AS and MR were also significantly different for the proportion of time to peak (p = 0.018 for the catheter measurements), RI (p = 0.045 and p = 0.002 for the catheter and displacement, respectively), and AIx (p = 0.005 for the displacement waveform). Significance. These findings demonstrate the ability of our system to provide insights into cardiac conditions and support further development as a diagnostic/telehealth-based screening tool.

Automated summary

The researchers developed a non-contact imaging system to estimate carotid artery pressure displacement waveforms by calculating the vessel-induced deformation of the skin. Clinical study results showed high similarity between displacement waveforms and aortic catheter pressures, with no significant differences in pulse wave analysis metrics. These findings have important implications for diagnosing and conducting remote health screening for cardiac conditions.