## 2 Methods
### Dataset Acquisition  
The open-source database VitalDB [37, 38] was employed for LVET assessment in both types of signals: ABP and PPG. A total of up to 6000 individual high-fidelity recordings were gathered from surgical procedures conducted in the operating room. According to the authors, ABP was digitally recorded using Tram-Rac 4A SNUADC, boasting 14-bit resolution and a time resolution of 2 ms (sampling frequency of 500 Hz). For ECG and PPG signals, a Solar 8000 M device with digital storage capability was utilized, maintaining the same sampling frequency. The reference ejection time was measured based on the T-wave segment duration from the ECG signal, as described in the first section. From the clinical data file, an initial selection step was taken regarding patient recordings. The primary inclusion criteria encompassed the following:  
* The surgical position was \"supine\" to eliminate the influence of hydrostatic pressure on the arterial network. This position ensured that all blood vessels were situated on the same horizontal plane and at the same height as the heart. * The arterial line was placed in the \"left radial\" position because this vessel is physically closer to the left ventricular location. Consequently, the impact of wave propagation delay is minimized in ABP recordings. However, no information regarding the left or right hand was provided for PPG acquisition. * The age of the patients was over 20 years old. After this initial selection, approximately 430 cases met the criteria mentioned above. From this point, the electronic recordings were carefully reviewed by medical experts to ensure they contained readable ECG, ABP and PPG waveforms. Unfortunately, most of the data either had missing ECG waveforms or exhibited distortions in the QRS complex morphology, resulting in only 75 cases remaining for the final analysis. Signal visualization was carried out using MATLAB R2023a software after applying specific processing techniques. The electrocardiogram signal underwent filtering using a 2 nd order Butterworth low-pass filter with a cutoff frequency (F3 db) of 30 Hz. Similarly, for ABP and PPG waveforms, the same filter was applied, but with a F3 bd of 20 Hz. During the signal processing phase, it was observed that a weaker filtering stage more effectively preserved the fiducial points, especially DCW and TDW. Waveform regions were selected from the beginning of case preparation until the surgery began. In the electronic records, the onset of the surgery procedure was marked with a flag. After this point, we lacked additional information regarding surgery stress, except for basic simple numerical readings. Events such blood loss can significantly impact the hemodynamic state, which could strongly influence the results. Therefore, up to this point, only the vasomotor action of the anesthetics drug manifested. Due to large number of cardiac cycles, artifacts, missing values or waveform morphology deviations were presents. Usually, in the surgery room, these undesired effects occur from touching the patient or the connection plugs of the attached sensors during case preparation. In order to mitigate these inconsistencies, corrupted waveform regions were discarded from the patients' recording. For the artifact elimination procedure, regions were discarded based on medical expertise since certain morphologies cannot occur in the real world. The remaining regions after visual inspection were next analyzed via numerical means. The time duration of the afore-mentioned parameters was computed using an Intellectual Proprietary algorithm. Broadly, the algorithm combines first derivative analysis with logic rule decisions. For every cardiac cycle, it expects the appearance of defined fiducial points with the associated possible locations and also consistency. In this step, clusters of 10 consecutive beats were automatically analyzed via numerical means.