Myocardial Infarction

Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion.

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal p...

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in...

Multiscale feature enhanced gating network for atrial fibrillation detection.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and...

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat...

A Deep Learning Approach for Mental Fatigue State Assessment.

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate ...

Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.

BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and va...

A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

OBJECTIVE: In Long-Term Monitoring (LTM), noise significantly impacts the quality of the electrocardiogram (ECG), posing challenges for accurate diagn...

Development and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke.

OBJECTIVE: To develop and validate an explainable machine learning (ML) model predicting the risk of hemorrhagic transformation (HT) after intravenous...

End-to-end deep-learning model for the detection of coronary artery stenosis on coronary CT images.

PURPOSE: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending ar...

Chlorfenapyr-related delayed rhabdomyolysis: a case series.

INTRODUCTION: Chlorfenapyr, a broad-spectrum insecticide and acaricide of the pyrrole-class pesticides, can induce dizziness, fatigue, profuse sweatin...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators a...

mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks.

This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that balance high classification performance with robust pr...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-a...

Aspirin modulates inflammatory biomarkers in patients with subcortical silent brain infarcts.

INTRODUCTION: This study aimed to identify differences in the levels of inflammation-related biomarkers between patients with subcortical silent brain...

Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis.

Neointimal coverage and stent apposition, as assessed from intravascular optical coherence tomography (IVOCT) images, are crucial for optimizing percu...

Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.

We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R...

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely availab...

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.

BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatien...

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.

BACKGROUND: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many c...

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnos...