Myocardial Infarction

Clinical Triage of Individuals With Warning Symptoms of Imminent Cardiac Arrest.

BACKGROUND: At least 50% of individuals who suffer sudden cardiac arrest (SCA) have warning symptoms before their SCA, but these are not sufficient to predict imminent SCA (ISCA). We hypothesized that combining symptoms with clinical profile could effectively predict ISCA. METHODS: In 2 community-based studies of SCA in Oregon and California, survivors of SCA who had experienced warning symptoms a...

An Intelligent IoT-Compatible Arrhythmia Detection System Using a Hybrid Vision Transformer-LSTM Framework.

BACKGROUND: In electrocardiogram (ECG) signal classification, advanced IoT-compatible systems and medical signal processing solutions have become feasible due to the rapid growth of the Internet of Things (IoT). However, it is challenging to detect and classify arrhythmias because of the complex nature and large volumes of ECG data. OBJECTIVE: This article proposes a hybrid deep learning (DL) appr...

Mode-Selective Dual-Level Vibrational Perturbation Theory Assisted by Machine Learning for Rotational and Vibrational Spectra of Benzoic Acid and Aspirin.

The rigid-rotor/harmonic-oscillator model is often insufficient for unbiased comparison with high-resolution microwave data and infrared measurements ...

Development of the CASPER-Score: A bedside tool integrating AI-based ECG analysis with key clinical predictors to identify relevant coronary artery stenosis in patients after out-of-hospital cardiac arrest.

INTRODUCTION: Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality worldwide, frequently associated with acute coronary syndromes. Wh...

Impact of the COVID-19 pandemic on ischemic stroke hospitalizations and in-hospital outcomes in the United States: A survey-weighted cohort with predictive modeling.

INTRODUCTION: Ischemic stroke remains a leading cause of death in the United States, with the COVID-19 pandemic exacerbating disparities. Prior studie...

Current Advances and Navigating the Complexities of SVG-PCI.

Saphenous vein graft (SVG) percutaneous coronary intervention (PCI) remains technically challenging and clinically high risk due to the friable, throm...

Implementation of an ai-enabled multimodal emergency care system is associated with improved sudden cardiac death rescue outcomes in anyang.

Sudden Cardiac Death (SCD) remains a leading cause of mortality worldwide, with outcomes critically dependent on the effective implementation of the "...

A systematic evaluation of the reliability of feature selection methods and SHAP-based interpretability in machine learning models for arrhythmia analysis.

Arrhythmia is one of the most prevalent cardiovascular diseases worldwide. The classification of arrhythmias plays a major role in the diagnosis of he...

Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.

Ischemic heart disease remains a major contributor to mortality in Malaysia, with non-elective percutaneous coronary intervention (PCI) frequently per...

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Cardiovascular diseases have been the primary contributor to deaths worldwide, and hence, the need to detect arrhythmia from Electrocardiogram signals...

Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence.

Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocard...

The role of artificial intelligence in the diagnosis, risk stratification, and treatment of heart failure: A narrative review.

Heart failure (HF) is a complex, multifactorial, and difficult-to-treat syndrome. Over the past years, a concerning increase in its global prevalence,...

Preictal reduction in heart rate variability entropy is associated with functional/dissociative seizures and provides modest discrimination from epileptic seizures.

OBJECTIVE: Differentiating functional/dissociative seizures (FDS) from epileptic seizures (ES) remains clinically challenging, with limited electrocar...

ChemoCardioNet: An Explainable Multimodal Transformer for Early Prediction of Chemotherapy-Induced Cardiotoxicity.

Chemotherapy-induced cardiotoxicity (CIC) remains a major cause of morbidity and mortality among cancer survivors, and conventional monitoring often f...

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Cuffless blood pressure (BP) monitoring technologies, primarily based on pulse transit time (PTT) or photoplethysmography (PPG), frequently suffer fro...

Explainable ECG Analysis by Explicit Information Disentanglement With VAEs.

OBJECTIVE: The interpretation of electrocardiogram (ECG) signals is vital for diagnosis of cardiac conditions. Traditional methods rely on expert know...

Artificial intelligence-based automated sleep staging using heart rate variability: Assessment of performance and clinical prospects.

Background Some artificial intelligence models use heart rate variability (HRV) features to classify sleep stages. Estimation of HRV indices requires ...

Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, affects 2% to 4% of the global adult population and is associated with an...

Deep learning analysis of ECGs detects Cardiovascular-Kidney-Metabolic syndrome burden in people with diabetes: a report from the Silesia Diabetes-Heart Project.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome refers to the co-occurrence of obesity, diabetes, chronic kidney disease (CKD), and cardiov...

Statin adherence among patients with hypertension in Swedish primary health care: a QregPV cohort study using explainable machine learning.

AIMS: Evidence regarding statin adherence among patients with hypertension in primary care is limited. We assessed statin adherence in Swedish primary...