Removing Drift from Carotid Arterial Pulse Waveforms: A Comparison of Motion Correction and High-Pass Filtering

Abstract

Non-invasive methods for estimating carotid artery (CA) pressure waveforms have been recently developed as a tool to detect heart abnormalities. Among non-invasive techniques, camera-based methods have the advantage of being noncontact, which enables measurements without applying external pressures to the artery. Camera-based methods measure skin deformation waveforms caused by arterial blood flow, which are assumed to have a similar shape to the pressure waveforms. Video recordings of the subject’s neck are analysed to quantify skin deformations caused by the carotid pressure pulse. However, in practice, unrelated motion, such as the relative movements of the camera and the subject, or movements due to breathing, can confound the skin measurements. One of the primary effects of this error is seen in the form of signal drift, which can make it difficult to analyse the shape of the skin displacement waveform. In this paper, we have investigated and compared two methods for removing the signal drift. One is to correct for the motion in the captured videos of the neck, and the second method is to use a highpass wavelet filter. The results showed that, although both methods could reduce the signal drift, they had dissimilar effects on the shape of the CA displacement waveforms. The high-pass wavelet filter seemed to preserve the original measured shape of the CA displacement waveforms better than the motion-correction method. However, in this study, it was not possible to quantify the performance since the true shape of the CA displacement waveforms was not known.