Before revision:  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\%.
After revision:  For 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 . Support vector machine (SVM) shows classification accuracyof 99.8\%.