Improved human pulse peak estimation using derivative features for noncontact pulse transit time measurements

This paper proposes a method to estimate temporally accurate human pulse peaks for noncontact pulse transit time (PTT) measurements. The PTT is considered as a significant diagnostic index for conditions such as blood pressure and arterial stiffness; however, millisecond-order accuracy is required in the determination of each pulse peak. In this study, human pulse waveforms are obtained from wrist and ankle images taken using a webcam at 90 cm distance. In the proposed method, the waveform is smoothed using finite impulse response low-pass filtering that sustains the shape of the pulse waveform, and the phase delay is compensated. Then, features of the first-order derivative of the filtered waveform are used to estimate the pulse peaks. The interbeat intervals obtained from the peaks estimated by the proposed method closely coincided with those obtained from a contact-type photoplethysmogram sensor, yielding less absolute error than that obtained from a comparative method; thus, this confirms the improved temporal accuracy of the proposed method. The PTTs are calculated from the time differences between the estimated pulse peaks of the wrist and those of the ankle images. The benefit of accurate pulse peak estimation is demonstrated by investigating the relation between the PTT and blood pressure. The PTTs are correlated with blood pressure in ten human participants, and a high correlation coefficient of -0.88 was obtained, indicating a direct relation between these two measures.