Myocardial Infarction

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a significantly high false positive rates (FPRs) when applied on ECG data collected under free-living ambulatory conditions and in the presence of non-AF ...

ECG classification system based on multi-domain features approach coupled with least square support vector machine (LS-SVM).

Developing a robust authentication and identification method becomes an urgent demand to protect the integrity of devices data. Although the use of passwords provides an acceptable control and authentication, it has shown much weakness in terms of speed and integrity, which make biometrics the ideal authentication solution. As a result, electrocardiogram (ECG) signals have received a great attenti...

[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 2 : Recent studies and future].

While fundamental aspects of the application of artificial intelligence (AI) to electrocardiogram (ECG) analysis were discussed in part 1 of this revi...

[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 1 : Basic principles].

Even though electrocardiography is a diagnostic procedure that is now more than 100 years old, medicine cannot do without it. On the contrary, interes...

rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with ec...

ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors.

In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wea...

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the...

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare...

A deep learning approach identifies new ECG features in congenital long QT syndrome.

BACKGROUND: Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routi...

Electrocardiogram Biometrics Using Transformer's Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification.

The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not ...

Sleep staging classification based on a new parallel fusion method of multiple sources signals.

In the field of medical informatics, sleep staging is a challenging and time consuming task undertaken by sleep experts. The conventional method for s...

Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals.

Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI, accurate electrocardiography (ECG) is important for timely diag...

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing.

Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable...

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiol...

Machine learning-based heart disease diagnosis: A systematic literature review.

Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of mach...

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach.

Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstrati...

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academ...

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablat...

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-t...

Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are h...