Objective. Understanding cardiac hemodynamic status (CHS) is essential for accurate cardiovascular health assessment, as it is governed by key parameters such as cardiac output (CO), systemic vascular resistance (SVR), and arterial compliance (AC). This study aims to develop a non-invasive method using digital photoplethysmography (PPGD) signals and deep learning techniques to predict these biomarkers for a comprehensive CHS evaluation. Approach. A dataset of 4374 virtual subjects was used. Nonlinear features were extracted from PPGD signals to capture their inherent complexity and irregularity. A parallel convolutional neural network (PCNN) was implemented to process both raw signals and nonlinear features concurrently. Model performance was evaluated using R ², root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). Main results. The PCNN demonstrated satisfactory predictive performance with R ², RMSE, MSE, and MAE values of 0.872, 0.086, 0.008, and 0.068 for CO; 0.851, 0.074, 0.006, and 0.058 for SVR; and 0.938, 0.049, 0.003, and 0.038 for AC. The proposed PCNN-based method offers a novel, non-invasive approach for predicting key cardiovascular biomarkers, providing an accurate CHS assessment. Significance. This method advances non-invasive cardiovascular diagnostics by combining PPGD signals and deep learning. Future work will focus on validating this findings in real-world settings for improved clinical applicability.

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm², respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries.

This study introduces the cardiac hemodynamic status (CHS), an interpretable index combining cardiac output, systemic vascular resistance, and arterial compliance into a single polar representation. CHS separates arterial stiffness orientation from overall hemodynamic load. The angle (CHS _θ ) discriminates healthy versus pathological states (AUC = 0.866), whereas the magnitude reflects flow-resistance balance. This Windkessel-consistent index offers a practical tool for cardiovascular screening and longitudinal monitoring, with potential for bedside estimation from common biosignals.

With aging being a major non-reversible risk factor for cardiovascular disease, the concept of Vascular Age (VA) emerges as a promising alternate measure to assess an individual’s cardiovascular risk and overall health. This study investigated the use of frequency features and Supervised Learning (SL) models for estimating a VA Age-Group (VAAG), as a surrogate of Chronological Age (CHA). Frequency features offer an accessible alternative to temporal and amplitude features, reducing reliance on high sampling frequencies and complex algorithms. Simulated subjects from One-dimensional models were employed to train SL algorithms, complemented with healthy in vivo subjects. Validation with real-world subject data was emphasized to ensure model applicability, using well-known risk factors as a form of cardiovascular health analysis and verification. Random Forest (RF) proved to be the best-performing model, achieving an accuracy/AUC score of 66.5%/0.59 for the in vivo test dataset, and 97.5%/0.99 for the in silico one. This research contributed to preventive medicine strategies, supporting early detection and personalized risk assessment for improved cardiovascular health outcomes across diverse populations.