  We investigated the beat-to-beat fluctuation of the photoplethysmography
(PPG) waveform. The motivation is that morphology variability extracted from
the arterial blood pressure (ABP) has been found to correlate with baseline
condition and short-term surgical outcome of the patients undergoing liver
transplant surgery. Numerous interactions of physiological mechanisms
regulating the cardiovascular system could underlie the variability of
morphology. We used the unsupervised manifold learning algorithm, Dynamic
Diffusion Map, to quantify the multivariate waveform morphological variation.
Due to the physical principle of light absorption, PPG waveform signals are
more susceptible to artifact and are nominally used only for visual inspection
of data quality in clinical medical environment. But on the other hand, the
noninvasive, easy-to-use nature of PPG grants a wider range of biomedical
application, which inspired us to investigate the variability of morphology
information from PPG waveform signal. We developed data analysis techniques to
improve the performance and validated with the real-life clinical database.
