Myocardial Infarction

A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG Signals via Continuous Wavelet Transform Images.

Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preserving system that accurately classifies pain levels (low, medium, and high) by leveraging the intricate relationship between pain perception and autonomic...

Hypoglycemia Prediction in Type 1 Diabetes With Electrocardiography Beat Ensembles.

INTRODUCTION: Current methods to detect hypoglycemia in type 1 diabetes (T1D) require invasive sensors (ie, continuous glucose monitors, CGMs) that generally have low accuracy in the hypoglycemic range. A forward-looking alternative is to monitor physiological changes induced by hypoglycemia that can be measured non-invasively using, eg, electrocardiography (ECG). However, current methods require ...

Deep Learning Approach for Automatic Heartbeat Classification.

Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram ...

Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food.

The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critica...

The Evolving Paradigm of Myocardial Infarction in the Era of Artificial Intelligence.

The classification and treatment of myocardial infarction (MI) have evolved significantly over the past few decades, with the ST-segment elevation myo...

Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.

BACKGROUND: Haemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challengi...

EffNet: an efficient one-dimensional convolutional neural networks for efficient classification of long-term ECG fragments.

Early Diagnosis of Cardiovascular disease (CVD) is essential to prevent a person from death in case of a cardiac arrhythmia. Automated ECG classificat...

Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patients.

BACKGROUND: Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utili...

AI-Cirrhosis-ECG (ACE) score for predicting decompensation and liver outcomes.

BACKGROUND & AIMS: Accurate prediction of disease severity and prognosis are challenging in patients with cirrhosis. We evaluated whether the deep lea...

Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients.

BACKGROUND: Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence e...

Temporal and spatial self supervised learning methods for electrocardiograms.

The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-su...

Antifreezing Ultrathin Bioionic Gel-Based Wearable System for Artificial Intelligence-Assisted Arrhythmia Diagnosis in Hypothermia.

Cardiovascular disease (CAD) is a major global public health issue, with mortality rates being significantly impacted by cold temperatures. Stable and...

Classification patterns identification of immunogenic cell death-related genes in heart failure based on deep learning.

Heart failure (HF) is a complex and prevalent condition, particularly in the elderly, presenting symptoms like chest tightness, shortness of breath, a...

Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms.

Adhesive epidermal hydrogel electrodes are essential for achieving robust signal transduction and cardiac arrhythmia diagnosis, but detachment of conv...

A machine learning based death risk analysis and prediction of ST-segment elevation myocardial infarction (STEMI) patients.

Acute myocardial infarction is a condition in which a part of the heart muscle cannot receive enough blood due to the narrowing and blockage of the ve...

Transformer-based heart language model with electrocardiogram annotations.

This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, a...

An arrhythmia classification using a deep learning and optimisation-based methodology.

The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transfo...

Prognostic Implications of Machine Learning Algorithm-Supported Diagnostic Classification of Myocardial Injury Using the Fourth Universal Definition of Myocardial Infarction.

BACKGROUND: With widespread adoption of high-sensitivity troponin assays, more individuals with myocardial injury are now identified, with type 1 myoc...

Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy.

Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of...

A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.

Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter met...