Cardiovascular

Myocardial Infarction

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

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ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning.

Biometric technologies offer much convenience over the conventional approaches to identity recogniti...

Phase-domain Deep Patient-ECG Image Learning for Zero-effort Smart Health Security.

Smart health is quickly boosted by technological advancements: smart sensors, body sensor network, i...

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great signific...

Novel Deep Convolutional Neural Network for Cuff-less Blood Pressure Measurement Using ECG and PPG Signals.

Cuff-less blood pressure (BP) is a potential method for BP monitoring because it is undisturbed and ...

An Electrocardiogram Delineator via Deep Segmentation Network.

Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which con...

A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform.

Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of gre...

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG ...

Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals.

Atrial fibrillation (AF) and atrial flutter (AFL) represent atrial arrhythmias closely related to in...

Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

The classification of the heartbeat type is an essential function in the automatical electrocardiogr...

Detection and Classification of Chronic Total Occlusion lesions using Deep Learning.

Cardiovascular disease (CVD) is one of the diseases with the highest mortality rate in modern societ...

[Automatic classification method of arrhythmia based on discriminative deep belief networks].

Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) s...

[Deep residual convolutional neural network for recognition of electrocardiogram signal arrhythmias].

Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphol...

[Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network].

OBJECTIVE: To classify Right Bundle Branch Block (RBBB),Left Bundle Branch Block (LBBB) and normal E...

A new deep learning model for assisted diagnosis on electrocardiogram.

In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep lea...

Detection of Left Ventricular Hypertrophy Using Bayesian Additive Regression Trees: The MESA.

Background We developed a new left ventricular hypertrophy ( LVH ) criterion using a machine-learnin...

Robotic PCI: Evolving from novel toward non-inferior.

Robotic-assisted PCI appears to be safe and feasible in both simple and complex lesions. In this sma...

[A DenseNet-based diagnosis algorithm for automated diagnosis using clinical ECG data].

OBJECTIVE: To train convolutional networks using multi-lead ECG data and classify new data accuratel...

A Cardiac Troponin T Biosensor Based on Aptamer Self-assembling on Gold.

In this study, a sensitive and accurate aptasensor was designed for early detection of myocardial in...

Estimating Systolic Blood Pressure Using Convolutional Neural Networks.

Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which in...

Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning.

The creation of big clinical data cohorts for machine learning and data analysis require a number of...

An Ontology Approach for Knowledge Representation of ECG Data.

The number of features that can be extracted from ECG signals has increased with the advancement in ...

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