Cardiovascular

Arrhythmias

Latest AI and machine learning research in arrhythmias for healthcare professionals.

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Showing 778-798 of 1,713 articles
Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.

PURPOSE: To develop a biological dose prediction model considering tissue bio-reactions in addition ...

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System.

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable rem...

Substrate-Free Multilayer Graphene Electronic Skin for Intelligent Diagnosis.

Current wearable sensors are fabricated with substrates, which limits the comfort, flexibility, stre...

Temporary pacemaker insertion for severe bradycardia following pneumoperitoneum during robot-assisted radical prostatectomy: a case report.

BACKGROUND: Pneumoperitoneum to maintain a constant gas flow to assist various surgeries is known to...

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design.

OBJECTIVE: Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome ...

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography.

BACKGROUND: In-hospital cardiac arrest is a major burden in health care. Although several track-and-...

Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images.

PURPOSE: The main purpose of this study is to construct a system to track the tumor position during ...

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms.

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive d...

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model.

Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of ab...

Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series.

Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associ...

FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network.

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends i...

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.

BACKGROUND AND OBJECTIVE: Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinica...

Pattern Recognition of Cognitive Load Using EEG and ECG Signals.

The matching of cognitive load and working memory is the key for effective learning, and cognitive e...

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis.

INTRODUCTION: Electrode misplacement and interchange errors are known problems when recording the 12...

CNN and LSTM-Based Emotion Charting Using Physiological Signals.

Novel trends in affective computing are based on reliable sources of physiological signals such as E...

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead el...

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