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  • Title: Effects of cuff inflation and deflation on pulse transit time measured from ECG and multi-wavelength PPG.
    Author: Liu J, Li Y, Ding XR, Dai WX, Zhang YT.
    Journal: Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():5973-6. PubMed ID: 26737652.
    Abstract:
    Pulse transit time (PTT), which refers to the time it takes a pulse wave to travel between two arterial sites is a promising index for cuff-less blood pressure (BP) estimation, as well as non-invasive assessment of arterial functions. However, it has not been investigated whether PTTs measured from ECG and different wavelength PPG are equally affected by the arterial status. Furthermore, comparison between the changes of different PTTs can provide enlightenment on the hardware implementation of the PTT-based BP estimation method. This work mainly studied the changes of PTTs calculated from electrocardiogram (ECG) and multi-wavelength photoplethysmogram (PPG) after exerting cuff pressure on the upper arm. A four-channel PPG acquisition system was developed to collect the multi-wavelength PPG signals of red, yellow, green and blue light at the fingertip simultaneously. Ten subjects participated in the experiment and their PTTs measured from different PPG and ECG signals before and after exerting cuff pressure were compared. This study found that within one minute after the four-minute cuff inflation and deflation process, the PTT measured from ECG and yellow PPG experienced a significant increase (p<;0.05) while the PTT from ECG and blue PPG had no statistical difference (p>0.9) compared with that before exerting cuff pressure. This indicates that PTTs calculated from different wavelength PPG have different recoverability from smooth muscle relaxation. Another interesting finding is that the PTT calculated from ECG and yellow PPG had a strong correlation (|r|>0.7) with the time difference between yellow PPG and other PPG signals, which implies the potential of the time difference between yellow PPG and other PPGs as a complementary to PTT-based model for blood pressure estimation.
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