Myocardial Infarction

Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.

PURPOSE: To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less burden on potential patients. The specific objective of this study was to verify the effectiveness of past and future single-lead electrocardiogram (ECG) data from SA occurrence sites in improving the estimation accuracy of SA and sleep apnea syndrome (SAS) using machine learning.

Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and segmentation techniques.

Conventional manual or feature-based ECG analysis methods are limited by time inefficiencies and human error. This study explores the potential of transforming 1D signals into 2D Gramian Angular Field (GAF) images for improved classification of four ECG categories: Atrial Fibrillation (AFib), Left Ventricular Hypertrophy (LVH), Right Ventricular Hypertrophy (RVH), and Normal ECG.•The study employe...

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an increasing mortality rate in recent years. Regular m...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from the traditional 24 h to 7-14 days, significantly en...

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by th...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making...

An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.

Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most of them have relatively low energy efficiency, and...

Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.

BACKGROUND: Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) is vital for clinical quality registries, performa...

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tac...

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the i...

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the risk of stroke and heart failure. Early detection is ...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiogr...

Artificial-intelligence-enhanced ECG: a potential solution to limitations of ECG in detecting left ventricular dysfunction after chemotherapy.

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in...

Synthetic ECG signal generation using generative neural networks.

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases. Additionally, the use of real patients' ECGs is h...

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods.

BACKGROUND: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinic...

Automated Identification of Stroke Thrombolysis Contraindications from Synthetic Clinical Notes: A Proof-of-Concept Study.

INTRODUCTION: Timely thrombolytic therapy improves outcomes in acute ischemic stroke. Manual chart review to screen for thrombolysis contraindications...

Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.

Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making ...

Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learning have facilitated the development of automated d...

Multimodal machine learning for deception detection using behavioral and physiological data.

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom trials. Differentiating truth from lies is inheren...