Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

7,963 articles

In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives.

Electrocardiography (ECG) remains an irreplaceable tool in the management of the patients with myoca...

Jun 2020 32624331

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG.

OBJECTIVE: The localization of origins of premature ventricular contraction (PVC) is the key factor ...

Jun 2020 32252035

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicin...

Jun 2020 32658725

Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the ...

Jun 2020 32506182

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

BACKGROUND: Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnorm...

Jun 2020 33328094

Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction.

Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in ...

May 2020 32439873

Bioinspired Soft Microrobots with Precise Magneto-Collective Control for Microvascular Thrombolysis.

New-era soft microrobots for biomedical applications need to mimic the essential structures and coll...

May 2020 32430939

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to s...

May 2020 32438582

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortalit...

May 2020 32509259

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardi...

May 2020 32406296

Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach.

The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LV...

May 2020 32401764

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces ...

May 2020 32393799

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beat...

May 2020 32388733

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

This study attempted to multimodally measure mental workload and validate indicators for estimating ...

May 2020 32330080

Cardiovascular

Myocardial Infarction

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In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives.

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG.

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction.

Bioinspired Soft Microrobots with Precise Magneto-Collective Control for Microvascular Thrombolysis.

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach.

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks.

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

Automatic diagnosis of the 12-lead ECG using a deep neural network.

Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review.

End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection.

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