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We investigated the beat-to-beat fluctuation of the photoplethysmography(PPG) waveform. The motivation is that morphology variability extracted fromthe arterial blood pressure (ABP) has been found to correlate with baselinecondition and short-term surgical outcome of the patients undergoing livertransplant surgery. Numerous interactions of physiological mechanismsregulating the cardiovascular system could underlie the variability ofmorphology. We used the unsupervised manifold learning algorithm, DynamicDiffusion Map, to quantify the multivariate waveform morphological variation.Due to the physical principle of light absorption, PPG waveform signals aremore susceptible to artifact and are nominally used only for visual inspectionof data quality in clinical medical environment. But on the other hand, thenoninvasive, easy-to-use nature of PPG grants a wider range of biomedicalapplication, which inspired us to investigate the variability of morphologyinformation from PPG waveform signal. We developed data analysis techniques toimprove the performance and validated with the real-life clinical database.

Variability of morphology in beat-to-beat photoplethysmographic waveform  quantified with unsupervised wave-shape manifold learning for clinical  assessment