Cardiovascular

Myocardial Infarction

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

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A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification.

This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN...

DDCNN: A Deep Learning Model for AF Detection From a Single-Lead Short ECG Signal.

With the popularity of the wireless body sensor network, real-time and continuous collection of sing...

Ensemble classification combining ResNet and handcrafted features with three-steps training.

This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardio...

A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks.

As the number of people suffering from cardiovascular diseases increases every year, it becomes esse...

Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data.

Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for ...

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart ...

A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram.

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogra...

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images.

The level of patient's illness is determined by diagnosing the problem through different methods lik...

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation.

We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiogr...

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various he...

Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.

The high number of fatal crashes caused by driver drowsiness highlights the need for developing reli...

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.

Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automa...

Analysis of Therapeutic Effect of Elderly Patients with Severe Heart Failure Based on LSTM Neural Model.

In recent years, cardiovascular-related diseases have become the "number one killer" threatening hum...

Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.

BACKGROUND: The application of artificial intelligence to interpret the electrocardiogram (ECG) has ...

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings.

We have developed deep learning models for automatic identification of the maternal heart rate (MHR)...

A review of arrhythmia detection based on electrocardiogram with artificial intelligence.

INTRODUCTION: With the widespread availability of portable electrocardiogram (ECG) devices, there wi...

A lightweight hybrid deep learning system for cardiac valvular disease classification.

Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical ...

Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction.

The performance and clinical implications of the deep learning aided algorithm using electrocardiogr...

Visualization deep learning model for automatic arrhythmias classification.

With the improvement of living standards, heart disease has become one of the common diseases that t...

A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Sleep is one of the most important human physiological activities, and plays an essential role in hu...

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification.

The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosi...

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