Boosting Algorithms based Cuff-less Blood Pressure Estimation from Clinically Relevant ECG and PPG Morphological Features.

Blood Pressure (BP) is often coined as a critical physiological marker for cardiovascular health. Multiple studies have explored either Photoplethysmogram (PPG) or ECG-PPG derived features for continuous BP estimation using machine learning (ML); deep learning (DL) techniques. Majority of those derived features often lack a stringent biological explanation and are not significantly correlated with BP.

Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative.

The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features.

Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition Technique and lightweight CNN Module.

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. High-quality PPG signals are necessary to extract cardiores-piratory information accurately. Motion artifacts can easily corrupt PPG signals due to human locomotion, leading to noise enriched, poor quality signals.

Development on Linearizing Front End and Amplification Structure for Commercial GMR Sensor-based Cardiorespiratory Monitoring system.

Magnetoplethysmogram (MPG) is typically acquired by placing a giant magnetoresistance sensor (GMR)-magnet system in a blood vessel's (e.g., radial artery) vicinity.

Introduction of Boosting Algorithms in Continuous Non-Invasive Cuff-less Blood Pressure Estimation using Pulse Arrival Time.

Blood Pressure (BP) is a critical biomarker for cardiorespiratory health. Conventional non-invasive BP measurement devices are mostly built on the principle of auscultation, oscillometry, or tonometry. The strong correlation between the Pulse Arrival Time (PAT) and BP has enabled unconstrained cuff-less BP monitoring.