Myocardial Infarction

Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care.

We aimed at reducing alarm fatigue in neonatal intensive care units by developing a model using machine learning for the early prediction of critical cardiorespiratory alarms. During this study in over 34,000 patient monitoring hours in 55 infants 278,000 advisory (yellow) and 70,000 critical (red) alarms occurred. Vital signs including the heart rate, breathing rate, and oxygen saturation were ob...

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE.

Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of electrocardiogram(ECG) signal are very small, which cannot be detected by human eyes. In this study, an 11-layer deep convolutional neural network model ...

High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.

INTRODUCTION: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on ...

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed a...

Artificial Intelligence Meets Chinese Medicine.

As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention. The sta...

A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Rece...

Clinical Significance of Circulating Cardiomyocyte-Specific Cell-Free DNA in Patients With Heart Failure: A Proof-of-Concept Study.

We investigated clinical significance of cell-free DNA (cfDNA) in heart failure. This study enrolled 32 heart failure patients and 28 control subjects...

The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS?

The ability to identify reliable and sensitive physiological signatures of psychological dimensions is key to developing intelligent adaptive systems ...

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predicti...

ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG.

BACKGROUND AND OBJECTIVE: Myocardial infarction (MI) is one of the most threatening cardiovascular diseases for human beings, which can be diagnosed b...

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition.

Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing hu...

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhy...

A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices.

Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, ea...

Clinical Study on Minimally Invasive Liquefaction and Drainage of Hypertensive Putaminal Hemorrhage through Frontal Approach.

 Hypertensive intracerebral hemorrhage is one of the most common cerebrovascular diseases with high mortality and high disability rate. The aim of th...

Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches.

BACKGROUND: Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data....

Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks.

This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach e...

Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway.

OBJECTIVE: To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers...

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized th...

Initial report of safety and procedure duration of robotic-assisted chronic total occlusion coronary intervention.

BACKGROUND: No previous reports have examined the impact of robotic-assisted (RA) chronic total occlusion (CTO) PCI on procedural duration or safety c...

Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs.

BACKGROUND: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related ...