A Machine Learning Methodology for estimation of vascular characteristics using a single carotid waveform

Cardiovascular diseases are the leading causes of morbidity and mortality. Methods for early detection of vascular impairment provide intuition into cardiovascular disease pathogenesis. For instance, any change in arterial compliance may lead up to the onset of a clinically recognizable disease, and by knowing that caregivers can identify patients at risk even before the onset of clinical signs and symptoms. It is notable that vascular network characteristics such as total arterial compliance (TAC) and aortic characteristic impedance (ACI) are among the key factors for cardiovascular disease detection. Challenges for noninvasive and single waveform evaluation of TAC and ACI have raised the demand for employing machine learning (ML) tools. This study presents an ML-based methodology for calculating TAC and ACI from a single carotid waveform. This method was trained and tested on a large human cohort with an age range from 19 to 90 (Framingham Heart Study). The dataset includes both healthy and patients with cardiovascular diseases (53% female). The final model was also tested on a set of clinical data blinded to all stages of development. This can be clinically significant since carotid waveforms can be captured noninvasively using tonometry-type devices or even an iPhone camera.