AIMC Topic: Models, Cardiovascular
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Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.
Scientific reports
Feb 11, 2025
The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking th...
Machine learning models based on FEM simulation of hoop mode vibrations to enable ultrasonic cuffless measurement of blood pressure.
Medical & biological engineering & computing
Jan 6, 2025
Blood pressure (BP) is one of the vital physiological parameters, and its measurement is done routinely for almost all patients who visit hospitals. Cuffless BP measurement has been of great research interest over the last few years. In this paper, w...
Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning.
Computers in biology and medicine
Dec 27, 2024
Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimati...
Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions.
Computers in biology and medicine
Dec 20, 2024
BACKGROUND: Physics-informed neural networks (PINNs) are increasingly being used to model cardiovascular blood flow. The accuracy of PINNs is dependent on flow complexity and could deteriorate in the presence of highly-dynamical blood flow conditions...
Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.
Computers in biology and medicine
Dec 12, 2024
Intraventricular vector flow mapping (VFM) is an increasingly adopted echocardiographic technique that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic const...
AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec 12, 2024
Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use...
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 27, 2024
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme using physics-informed neural networks (PINNs) and a ...
Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.
Computers in biology and medicine
Nov 7, 2024
Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impracti...
Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.
Annals of biomedical engineering
Oct 31, 2024
The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has emerged as a promising vehicle toward optimizing outcomes, where pre-operative measurements are used ...
Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature.
Medical image analysis
Oct 22, 2024
Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points...
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