AIMC Topic: Myocardial Contraction

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A deep convolutional neural network model to classify heartbeats.

Computers in biology and medicine Aug 24, 2017
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrh...
Neural Networks, Computer Databases, Factual Female Humans Models, Cardiovascular Myocardial Contraction Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Male Electrocardiography
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An Implantable Extracardiac Soft Robotic Device for the Failing Heart: Mechanical Coupling and Synchronization.

Soft robotics May 30, 2017
Soft robotic devices have significant potential for medical device applications that warrant safe synergistic interaction with humans. This article describes the optimization of an implantable soft robotic system for heart failure whereby soft actuat...
Heart-Assist Devices Cardiac Output Biomedical Engineering Heart Heart Failure Myocardial Contraction Biomechanical Phenomena Animals Disease Models, Animal Robotics Swine Humans
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Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification.

The international journal of cardiovascular imaging Mar 20, 2017
The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to...
Humans Echocardiography, Doppler, Color Machine Learning Myocardial Contraction Automation Biomechanical Phenomena Pattern Recognition, Automated Predictive Value of Tests Ventricular Function, Left Principal Component Analysis Reproducibility of Results Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Myocardial Infarction Observer Variation Severity of Illness Index Time Factors Case-Control Studies Magnetic Resonance Imaging
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Predicting the physiological response of Tivela stultorum hearts with digoxin from cardiac parameters using artificial neural networks.

Bio Systems Nov 15, 2016
Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were th...
Cardiotonic Agents Models, Cardiovascular Myocardial Contraction Digoxin Bivalvia Neural Networks, Computer Animals Algorithms Signal Processing, Computer-Assisted Heart
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Modeling and analysis of bio-syncretic micro-swimmers for cardiomyocyte-based actuation.

Bioinspiration & biomimetics Aug 22, 2016
Along with sensation and intelligence, actuation is one of the most important factors in the development of conventional robots. Many novel achievements have been made regarding bio-based actuators to solve the challenges of conventional actuation. H...
Hydrodynamics Equipment Design Biomimetic Materials Animals Artificial Intelligence Magnetics Movement Myocytes, Cardiac Models, Theoretical Dolphins Biomechanical Phenomena Myocardial Contraction Sensation Robotics Swimming
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Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features.

Journal of the American College of Cardiology Aug 25, 2020
BACKGROUND: Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.
Echocardiography Middle Aged ROC Curve Male Stroke Volume Machine Learning Early Diagnosis Signal Processing, Computer-Assisted Female Heart Failure, Diastolic Ventricular Dysfunction, Left Humans Myocardial Contraction Predictive Value of Tests
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