Myocardial Infarction

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based analysis of 12-lead electrocardiograms (ECGs) can already detect arrhythmias and hypertension. We performed an observational two-center study in order to develop a machine learning algorithm to proactively detect arterial hypertension from single-lead ...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated heart change, it often fails to detect early subtle structural changes. Electrocardiogram(ECG) represents electrical activity of heart muscle, affected by the changes in heart's structure. It is crucial to explore whether ECG can capture slight signa...

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by...

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk f...

Which Variables Better Predict Phonemic Awareness of First Grade Primary School Students?

The purpose of the research was to discover which variables better predict phonemic awareness. Socioeconomic status (SES), quality of parent-child int...

Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods.

Feature attribution methods can visually highlight specific input regions containing influential aspects affecting a deep learning model's prediction....

An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm.

Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this ...

Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients.

BACKGROUND: Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for m...

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as d...

Deep residual 2D convolutional neural network for cardiovascular disease classification.

Cardiovascular disease (CVD) continues to be a major global health concern, underscoring the need for advancements in medical care. The use of electro...

Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic valu...

An analysis regarding the article "Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis".

Cardiac Amyloidosis (CA) occurs when misfolded proteins accumulate in the heart muscle, leading to restrictive cardiomyopathy and possibly escalating ...

CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems.

The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional auth...

Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis.

BACKGROUND: Diagnosis of cardiac amyloidosis (CA) is often delayed due to variability in clinical presentation. The electrocardiogram (ECG) is one of ...

Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmog...

Effective cardiac disease classification using FS-XGB and GWO approach.

Globally, cardiovascular diseases (CVDs) are a leading cause of death; however, their impact can be greatly mitigated by early detection and treatment...

Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) benefit from prompt primary percutaneous intervention...

Image-based ECG analyzing deep-learning algorithm to predict biological age and mortality risks: interethnic validation.

BACKGROUND: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we dev...

The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery.

BACKGROUND: Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The anomalous origin of the coronary artery may lead to p...

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection.

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record ...