  Cardiovascular waveforms contain information for clinical diagnosis. By
"learning" and organizing the subtle change of waveform morphology from large
amounts of raw waveform data, unsupervised manifold learning helps delineate a
high-dimensional structure and display it as a novel three-dimensional (3D)
image. We investigate the electrocardiography (ECG) waveform for ischemic heart
disease and arterial blood pressure (ABP) waveform in dynamic vasoactive
episodes. We model each beat or pulse to be a point lying on a manifold, like a
surface, and use the diffusion map (DMap) to establish the relationship among
those pulses. For ECG datasets, first we analyzed the non-ST-elevation ECG
waveform distribution from unstable angina to healthy control, and we
investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG
waveform changes. For ABP datasets, we analyzed waveforms collected under
endotracheal intubation and administration of vasodilator. To quantify the
dynamic separation, we applied the support vector machine (SVM) analysis and
the trajectory analysis. For the non-ST-elevation ECG, a hierarchical tree
structure comprising consecutive ECG waveforms spanning from unstable angina to
healthy control is presented in the 3D image (accuracy=97.6%, macro-F1=96.1%).
The DMap helps quantify and visualize the evolving direction of intraoperative
ST-elevation myocardial episode in a 1-hour period (accuracy=97.58%,
macro-F1=96.06%). The ABP waveform analysis of Nicardipine administration shows
inter-individual difference (accuracy=95.01%, macro-F1=96.9%) and their common
directions from intra-individual moving trajectories. The dynamic change of the
ABP waveform during endotracheal intubation shows a loop-like trajectory
structure, which can be further divided using the knowledge obtained from
Nicardipine. The 3D images provide clues of underneath physiological
mechanisms.
