  Hypertension, a leading contributor to cardiovascular morbidity, underscores
the need for accurate and continuous blood pressure (BP) monitoring.
Photoplethysmography (PPG) presents a promising approach to this end. However,
the precision of BP estimates derived from PPG signals has been the subject of
ongoing debate, necessitating a comprehensive evaluation of their effectiveness
and constraints. We developed a calibration-based Siamese ResNet model for BP
estimation, using a signal input paired with a reference BP reading. We
compared the use of normalized PPG (N-PPG) against the normalized Invasive
Arterial Blood Pressure (N-IABP) signals as input. The N-IABP signals do not
directly present systolic and diastolic values but theoretically provide a more
accurate BP measure than PPG signals since it is a direct pressure sensor
inside the body. Our strategy establishes a critical benchmark for PPG
performance, realistically calibrating expectations for PPG's BP estimation
capabilities. Nonetheless, we compared the performance of our models using
different signal-filtering conditions to evaluate the impact of filtering on
the results. We evaluated our method using the AAMI and the BHS standards
employing the VitalDB dataset. The N-IABP signals meet with AAMI standards for
both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), with
errors of 1.29+-6.33mmHg for systolic pressure and 1.17+-5.78mmHg for systolic
and diastolic pressure respectively for the raw N-IABP signal. In contrast,
N-PPG signals, in their best setup, exhibited inferior performance than N-IABP,
presenting 1.49+-11.82mmHg and 0.89+-7.27mmHg for systolic and diastolic
pressure respectively. Our findings highlight the potential and limitations of
employing PPG for BP estimation, showing that these signals contain information
correlated to BP but may not be sufficient for predicting it accurately.
