Cardiovascular waveforms contain information for clinical diagnosis. By "learning" URLanizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning can help delineate a high-dimensional structure . We hypothesize that the shape of this structure conveys inner dynamics information via the novel three-dimensional (3D) image. To validate this hypothesis, we investigate the electrocardiography (ECG) waveform for ischemic heart disease and arterial blood pressure (ABP) waveform in dynamic vasoactive episodes. For non-ST-elevation ECG obtained from public database, a classification tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented . Support vector machine (SVM) shows classification accuracyof 99.8\%. Manifold learning in ST-elevation ECG waveform analysis helps quantify the evolving direction of  ST-elevation myocardial episode in a 1-hour period  . The ABP waveform analysis of Nicardipine administration shows inter-individual difference ( SVM classification accuracy= 99.3\% ) and their common directions from intra-individual moving trajectories. The dynamic change of  ABP waveform during endo-tracheal intubation shows a loop-like trajectory structure, which can be further classified using the knowledge of antihypertensive medication. The application of manifold learning in ECG and ABP waveform analysis provides clues of acute coronary syndrome diagnosis, predicts clinical course in myocardial ischemic episode and reveals underneath physiological mechanism via 3D visualization .
Cardiovascular waveforms contain information for clinical diagnosis. By "learning" URLanizing 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 .
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