Robust contactless pulse transit time estimation based on signal quality metric

The pulse transit time (PTT) can provide valuable insight into cardiovascular health, specifically regarding arterial stiffness and blood pressure. Traditionally, PTT is derived by calculating the time difference between two photoplethysmography (PPG) measurements, which require a set of body-worn sensors attached to the skin. Recently, remote photoplethysmography (rPPG) has been proposed as a contactless monitoring alternative. The main problem with rPPG based PTT estimation is that motion artifacts affect the shape of waveform leading to the shift or over-detected peaks, which decreases the accuracy of PTT. To overcome this problem, this paper presents a robust pulse-by-pulse PTT estimation framework using a signal quality metric. By exploiting the local temporal information and global periodic characteristics, the metric automatically assesses pulse quality of signal on a pulse-by-pulse basis, and calculates the probabilities of the pulse peak being the actual peak. Furthermore, in order to cope with over-detected and shift pulse peaks, Kalman filter complemented by the proposed signal quality metric is used to adaptively adjust the peaks based on the estimated probability. All the refined peaks are finally used for pulse-by-pulse PTT estimation. The experiment results are promising, suggesting that the proposed framework provides a robust and more accurate PTT estimation in real applications.