Arrhythmias

Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review.

INTRODUCTION: The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria s...

Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.

BACKGROUND: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals.

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases....

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead el...

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes wh...

ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs.

Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an important role in cardiovascular diagnosis and care. Whi...

Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery.

Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased hospital stay and costs. This study aimed to explor...

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some conc...

Deep learning based ECG segmentation for delineation of diverse arrhythmias.

Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart con...

Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated Electrocardiogram Signals Preprocessed by Adaptive Filtering Algorithm.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early d...

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular eve...

xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.

Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECG...

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due ...

ECG autoencoder based on low-rank attention.

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogr...

Multiscale dilated convolutional neural network for Atrial Fibrillation detection.

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality....

Fillable Magnetic Microrobots for Drug Delivery to Cardiac Tissues In Vitro.

Many cardiac diseases, such as arrhythmia or cardiogenic shock, cause irregular beating patterns that must be regulated to prevent disease progression...

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and strok...

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring appli...

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.

Quantitative imaging in life sciences has evolved into a powerful approach combining advanced microscopy acquisition and automated analysis of image d...

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D.

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly dim...