Arrhythmias

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch mode...

Edge computing-based ensemble learning model for health care decision systems.

A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment procedures that are both accurate and fast. Improved patient conditions and enhanced Decision-Making Systems (DMS) for healthcare professionals are the primary objectives of the Clinical Decision Support System (CDSS) recommended in this research artic...

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac ab...

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological ...

Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs.

BACKGROUND: Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to re...

AI derived ECG global longitudinal strain compared to echocardiographic measurements.

Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial...

Role of Artificial Intelligence-assisted Decision Support Tool for Common Rhythm Disturbances: A ChatGPT Proof-of-concept Study.

BACKGROUND: The objective of this article was to explore the use of ChatGPT as a clinical support tool for common arrhythmias.

Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network.

The classification of ECG signals is a critical process because it guides the diagnosis of the proper treatment process for the patient. However, any ...

Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardi...

Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach.

Noise detection in ambulatory electrocardiography is investigated as a machine learning binary classification problem on a set of twelve noise indices...

Machine Learning for Localization of Premature Ventricular Contraction Origins: A Review.

Premature ventricular contraction (PVC) is one of the most common arrhythmias, originating from ectopic beats in the ventricles. Precision in localizi...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.

PURPOSE OF REVIEW: To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias...

Non-specific myocardial fibrosis in young competitive athletes: clinical significance and risk prediction by a powerful machine learning-based model.

BACKGROUND: Non-specific myocardial fibrosis (NSMF) is a heterogeneous entity. We aimed to evaluate young athletes with and without NSMF to establish ...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated ...

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by...

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk f...

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiova...

Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods.

Feature attribution methods can visually highlight specific input regions containing influential aspects affecting a deep learning model's prediction....

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

BACKGROUND AND OBJECTIVE: The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hep...