Arrhythmias

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 (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study,...

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 interventions. In this paper, we present a Convolutional Neural Network (CNN) framework for deformable transesophageal US/CT image registration, for the cardiac arrhythmias, and guidance therapy purposes. The framework consists of a CNN, a spatial transformer, and a resampler. The CNN expects concat...

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 burning research topic in biomedical signal proces...

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 that leads to a serious life-threatening conditio...

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 of drug-induced repolarization disorders and show...

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, recognition of, and response to high-risk clinic...

Deep learning for comprehensive ECG annotation.

BACKGROUND: Increasing utilization of long-term outpatient ambulatory electrocardiographic (ECG) monitoring continues to drive the need for improved E...

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniatu...

[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 to induce life-threatening complications such as ...

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 slow. Rapid heartbeats can lead to shortness of bre...

Machine Intelligence in Cardiovascular Medicine.

The computer science technology trend called artificial intelligence (AI) is not new. Both machine learning and deep learning AI applications have rec...

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 rhythm monitoring, shedding light in recent progre...

[Endogenous nocciceptin/orphanin FQ affect ischemic arrhythmias in rats through Raf kinase inhibitor protein].

OBJECTIVE: To investigate whether endogenous nociceptin/orphanin FQ (N/OFQ) can inhibit arrhythmia and expression of β-adrenergic receptor (β-AR) on t...

[Heartbeat-based end-to-end classification of arrhythmias].

OBJECTIVE: We propose a heartbeat-based end-to-end classification of arrhythmias to improve the classification performance for supraventricular ectopi...

Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ...

Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals.

Atrial fibrillation (AF) and atrial flutter (AFL) represent atrial arrhythmias closely related to increasing risk for embolic stroke, and therefore be...

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of...

A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform.

Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of great clinical significance but at the same time it r...

An Electrocardiogram Delineator via Deep Segmentation Network.

Electrocardiogram (ECG) delineation is a process to detect multiple characteristic points, which contain critical diagnostic information about cardiac...

Convolutional Recurrent Neural Networks to Characterize the Circulation Component in the Thoracic Impedance during Out-of-Hospital Cardiac Arrest.

Pulse detection during out-of-hospital cardiac arrest remains challenging for both novel and expert rescuers because current methods are inaccurate an...