Arrhythmias

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.

Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in t...

Semantic Anomaly Detection in Medical Time Series.

The main goal of this project was to define and evaluate a new unsupervised deep learning approach t...

Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography.

Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can ...

A deep learning-based algorithm for detection of cortical arousal during sleep.

STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally,...

Symmetric Projection Attractor Reconstruction analysis of murine electrocardiograms: Retrospective prediction of Scn5a genetic mutation attributable to Brugada syndrome.

BACKGROUND: Life-threatening arrhythmias resulting from genetic mutations are often missed in curren...

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction...

Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal.

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a ...

Deformable US/CT Image Registration with a Convolutional Neural Network for Cardiac Arrhythmia Therapy.

Image registration represents one of the fundamental techniques in medical imaging and image-guided ...

Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM.

This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Ele...

A V-Net Based Deep Learning Model for Segmentation and Classification of Histological Images of Gastric Ablation.

Gastric motility disorders are associated with bioelectrical abnormalities in the stomach. Recently,...

RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG.

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applicati...

Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation.

Lacking sufficient training samples of different heart rhythms is a common bottleneck to obtain arrh...

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In th...

Emerging Concepts and Applied Machine Learning Research in Patients with Drug-Induced Repolarization Disorders.

The paper presents a review of current research to develop predictive models for automated detection...

Deep learning for comprehensive ECG annotation.

BACKGROUND: Increasing utilization of long-term outpatient ambulatory electrocardiographic (ECG) mon...

Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension.

BACKGROUND: Predictive analytics systems may improve perioperative care by enhancing preparation for...

Commentary: Laser Ablation of Abnormal Neurological Tissue Using Robotic Neuroblate System (LAANTERN): Procedural Safety and Hospitalization.

Can artificial intelligence optimize case selection for hemi-gland ablation?

[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy...

Contribution of neural networks in the diagnosis and treatment of cardiac arrhythmia.

Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very s...

The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms.

This review aims to provide a comprehensive recapitulation of the evolution in the field of cardiac ...