Cardiovascular

Myocardial Infarction

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

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Heart age estimated using explainable advanced electrocardiography.

Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesi...

Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks.

Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to t...

Safety and Efficacy of Robotic-Assisted PCI.

PURPOSE OF REVIEW: Robotics has been used in multiple areas of procedural medical intervention. Robo...

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-inter...

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve...

An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices.

Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-ef...

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...

[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 (E...

[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, medic...

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...

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 i...

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 res...

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 ...

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 undertak...

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 elec...

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 fr...

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 p...

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...

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