Myocardial Infarction

Enabling Clinical Trials of Artificial Intelligence: Infrastructure for Heart Failure Predictions.

The last decade has seen a large increase in artificial intelligence research within healthcare. However, relatively few attempts of clinical trials have been made for such configurations. One of the main challenges arise in the extensive infrastructure necessary, both for development, but particularly to run prospective studies. In this paper, infrastructural requirements are first presented, tog...

Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification.

Deep Learning architectures for time series require a large number of training samples, however traditional sample size estimation for sufficient model performance is not applicable for machine learning, especially in the field of electrocardiograms (ECGs). This paper outlines a sample size estimation strategy for binary classification problems on ECGs using different deep learning architectures a...

Fairness in Artificial Intelligence: Regulatory Sanbox Evaluation of Bias Prevention for ECG Classification.

As the use of artificial intelligence within healthcare is on the rise, an increased attention has been directed towards ethical considerations. Defin...

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. The aim of this study was to evaluate the factors 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 remain suboptimal. We assessed whether the electrocard...

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 $ > million people worldwide suffer from ChHD. Sudden ...

A review on multimodal machine learning in medical diagnostics.

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to pa...

[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 various clinical settings, with potentially extre...

[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 performance of a diagnostic test that has been in use f...

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 rises gradually year by year. Driven by the developmen...

[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 intervention of fetal abnormalities. In this pape...

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

AIMS: This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac...

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 either employ invasive electrophysiological (EP) map...

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 is facilitated by electrocardiography (ECG) device...

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

BACKGROUND: Although idiopathic Parkinson's disease (IPD) is increasing with the aging population, there is no adequate screening test for early diagn...

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 performed by measuring the electrocardiogram (ECG) signals...

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

BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning ana...

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 appropriate intervention. However, in existing detection ...

LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices.

Atrial Fibrillation (AF) is a kind of arrhythmia, which is a major morbidity factor, and AF can lead to stroke, heart failure and other cardiovascular...

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 critical CVD, can be easily detected from an electrocar...