Myocardial Infarction

MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network.

Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and management of cardiac conditions. However, the ECG signal is easily disrupted by various noise signals in the real world, leading to a decrease in signal quality and potentially compromising accurate clinical interpretation. With the goal of reducing n...

The Hemodynamic Management and Postoperative Outcomes After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Prospective Observational Study.

Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) has become standard treatment for peritoneal cancers and metastases, significantly enhancing survival rates. This study evaluated the relationship between tumor burden, hemodynamic management, and postoperative outcomes after CRS-HIPEC. This study included 203 patients undergoing CRS-HIPEC. The study was registered ...

Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.

This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-...

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in c...

Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this ...

Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability and generalization constraints.

The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation has traditionally relied on cardiologists' exper...

Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of impro...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology.

BACKGROUND: Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficiency, scalability, and insight generation. In cardiol...

An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neu...

Influence of renal function on blood pressure control and outcome in thrombolyzed patients after acute ischemic stroke: analysis of the ENCHANTED trial.

BACKGROUND: The effect of renal impairment in patients who receive intravenous thrombolysis for acute ischemic stroke (AIS) is unclear. We aimed to de...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worl...

Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation.

BACKGROUND: Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a prevalence of 1%-2%. AF is also associated with an incr...

Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction.

Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and precise diagnosis. The enhancement of automated m...

Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

BACKGROUND: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assessment. While deep learning algorithms have shown pr...

An ECG-based machine-learning approach for mortality risk assessment in a large European population.

AIMS: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on ...

Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System.

Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals is challenging. However, long-duration ECG data a...

A hybridization of XGBoost machine learning model by Optuna hyperparameter tuning suite for cardiovascular disease classification with significant effect of outliers and heterogeneous training datasets.

BACKGROUND: Over the last few decades: heart disease (HD) has emerged as one of the deadliest diseases in the world. Approximately more than 31 % of t...

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopathy (HCM) on 12-lead ECGs and offers a novel way to...

Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms.

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the leading causes of morbidity and mortality. Revasculari...