Myocardial Infarction

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 (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the f...

MedYOLO: A Medical Image Object Detection Framework.

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks...

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....

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...

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).

The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical re...

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...

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...

ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.

Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are crucial for diagnosing them. Traditionally, ECGs ar...

Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely...

Robustness of Deep Learning models in electrocardiogram noise detection and classification.

BACKGROUND AND OBJECTIVE: Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy l...

A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure.

Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to bes...

Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.

Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduce...

Exploring a new frontier in cardiac diagnosis: ECG analysis enhanced by machine learning and parametric quartic spline modeling.

The heart's study holds paramount importance in human physiology, driving valuable research in cardiovascular health. However, assessing Electrocardio...

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients.

BACKGROUND: Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. ...

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.

PURPOSE OF REVIEW: Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabi...

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) cha...

A lightweight deep learning approach for detecting electrocardiographic lead misplacement.

. Electrocardiographic (ECG) lead misplacement can result in distorted waveforms and amplitudes, significantly impacting accurate interpretation. Alth...

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside pers...