  The presented study aims to estimate blood pressure (BP) using
photoplethysmogram (PPG) signals while employing multiple machine learning
models. The study proposes a novel algorithm for signal reconstruction, which
utilizes the semi-classical signal analysis (SCSA) technique. The proposed
algorithm optimises the semi-classical constant and eliminates the trade-off
between complexity and accuracy in reconstruction. The reconstructed signals'
spectral features are extracted and incorporated with clinically relevant PPG
and its second derivative's (SDPPG) morphological features. The developed
method was assessed using a publicly available virtual in-silico dataset with
more than 4000 subjects, and the Multi-Parameter Intelligent Monitoring in
Intensive Care Units dataset. Results showed that the method attained a mean
absolute error of 5.37 and 2.96 mmHg for systolic and diastolic BP,
respectively, using the CatBoost supervisory algorithm. This approach met the
standards set by the Advancement of Medical Instrumentation, and achieved Grade
A for all BP categories in the British Hypertension Society protocol. The
proposed framework performs well even when applied to a combined database of
the MIMIC-III and the Queensland dataset. This study also evaluates the
proposed method's performance in a non-clinical setting with noisy and deformed
PPG signals, to validate the efficacy of the SCSA method. The noise stress
tests showed that the algorithm maintained its key feature detection, signal
reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. It
is believed that the proposed cuff-less BP estimation technique has the
potential to perform well on resource-constrained settings due to its
straightforward implementation approach.
