Myocardial Infarction

Benchmarking the Impact of Noise on Deep Learning-Based Classification of Atrial Fibrillation in 12-Lead ECG.

Electrocardiography analysis is widely used in various clinical applications and Deep Learning model...

FACTORS AFFECTING PROGNOSIS AND MORTALITY IN SEVERE COVID-19 PNEUMONIA PATIENTS.

Fatality rate in coronavirus disease 2019 (COVID-19) cases has been reported to be 3.4% worldwide. T...

Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography.

AIMS: Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients rema...

Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring.

About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > ...

A review on multimodal machine learning in medical diagnostics.

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complement...

Non-contact wearable synchronous measurement method of electrocardiogram and seismocardiogram signals.

Cardiovascular disease is one of the leading threats to human lives and its fatality rate still rise...

[Artificial intelligence applied to the electrocardiogram, or is there really a needle in a haystack?].

Artificial intelligence applied to the standard Ecg (Ai-Ecg) is able to enormously enhance the perfo...

[Why artificial intelligence applied to the electrocardiogram is not yet clinical routine?].

Artificial intelligence opens up multiple application scenarios to the electrocardiogram (Ai-Ecg) in...

[Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network].

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and...

Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning?

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.

AIMS: This study aims to identify and visualize electrocardiogram (ECG) features using an explainabl...

Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices.

AIMS: Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target ei...

Deep Learning Algorithm of 12-Lead Electrocardiogram for Parkinson Disease Screening.

BACKGROUND: Although idiopathic Parkinson's disease (IPD) is increasing with the aging population, t...

A novel method for conformity assessment testing of electrocardiographs for post-market surveillance purposes.

BACKGROUND: Monitoring cardiac parameters is the fundamental aspect of every diagnostic process and ...

Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks.

Automatic arrhythmia detection is very important for cardiovascular health. It is generally performe...

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.

BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and morta...

A two-step method for paroxysmal atrial fibrillation event detection based on machine learning.

Detection of atrial fibrillation (AF) events is significant for early clinical diagnosis and appropr...

A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) ...

A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography.

Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for he...

Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation.

Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets ...

Normal and Abnormal Classification of Electrocardiogram: A Primary Screening Tool Kit.

Cardiovascular diseases (CVDs) are one of the principal causes of death. Cardiac arrhythmia, a criti...