Abstract: Machine learning techniques for estimating an indication of carotid-femoral pulse wave velocity (CFPWV) and/or vascular age (VA) of a subject from an arterial waveform previously measured at a single site by an arterial waveform acquisition device coupled to the subject, the method comprising using at least one computer hardware processor to perform: obtaining the arterial waveform, the arterial waveform comprising a plurality of portions corresponding to a respective plurality of heartbeats of the subject; generating a representative heartbeat waveform from the arterial waveform; and providing the representative heartbeat waveform as input to a trained machine learning model to produce a corresponding output indicative of the CFPWV of the subject.

Abstract: Machine learning techniques for estimating an indication of carotid-femoral pulse wave velocity (CFPWV) and/or vascular age (VA) of a subject from an arterial waveform previously measured at a single site by an arterial waveform acquisition device coupled to the subject, the method comprising using at least one computer hardware processor to perform: obtaining the arterial waveform, the arterial waveform comprising a plurality of portions corresponding to a respective plurality of heartbeats of the subject; generating a representative heartbeat waveform from the arterial waveform; and providing the representative heartbeat waveform as input to a trained machine learning model to produce a corresponding output indicative of the CFPWV of the subject.

Abstract: Machine learning techniques for estimating an indication of carotid-femoral pulse wave velocity (CFPWV) and/or vascular age (VA) of a subject from an arterial waveform previously measured at a single site by an arterial waveform acquisition device coupled to the subject, the method comprising using at least one computer hardware processor to perform: obtaining the arterial waveform, the arterial waveform comprising a plurality of portions corresponding to a respective plurality of heartbeats of the subject; generating a representative heartbeat waveform from the arterial waveform; and providing the representative heartbeat waveform as input to a trained machine learning model to produce a corresponding output indicative of the CFPWV of the subject.