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Models, Cardiovascular

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Using machine learning to characterize heart failure across the scales.

Biomechanics and modeling in mechanobiology
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the p...

Smeared multiscale finite element model for electrophysiology and ionic transport in biological tissue.

Computers in biology and medicine
Basic functions of living organisms are governed by the nervous system through bidirectional signals transmitted from the brain to neural networks. These signals are similar to electrical waves. In electrophysiology the goal is to study the electrica...

Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.

BMC medical research methodology
BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML m...

A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.

International journal of medical informatics
PURPOSE: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.

Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning.

Annals of biomedical engineering
We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a generic left-ventricular (LV) endocardial surface in real time from the 12-lead ECG. The present stud...

A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects.

Medical & biological engineering & computing
This paper presents a unified approach based on the recurrence quantification analysis (RQA) and approximate entropy (ApEn) for the classification of heart rate variability (HRV). In this paper, the optimum tolerance threshold (r) corresponding to Ap...

Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.

Computers in biology and medicine
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often th...

Transfer learning for classification of cardiovascular tissues in histological images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical prac...

Machine learning-aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm.

International journal for numerical methods in biomedical engineering
Machine learning was applied to classify tension-strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of feat...

Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images.

IEEE transactions on bio-medical engineering
GOAL: We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone.