Electrocardiographic patch devices and contemporary wireless cardiac monitoring. [PDF]
, 2015 Cardiac electrophysiologic derangements often coexist with disorders of the circulatory system. Capturing and diagnosing arrhythmias and conduction system disease may lead to a change in diagnosis, clinical management and patient outcomes.
Carlson, SK +9 more
core +2 more sources
R-from-T as a common mechanism of arrhythmia initiation in long QT syndromes [PDF]
, 2019 Background: Long QT syndromes (LQTS) arise from many genetic and nongenetic causes with certain characteristic ECG features preceding polymorphic ventricular tachyarrhythmias (PVTs).
Liu, Michael B. +3 more
core +2 more sources
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals [PDF]
arXiv, 2021 Learning information-rich and generalizable representations effectively from unlabeled multivariate cardiac signals to identify abnormal heart rhythms (cardiac arrhythmias) is valuable in real-world clinical settings but often challenging due to its complex temporal dynamics.
arxiv
Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals [PDF]
arXiv, 2023 Electrocardiograms (ECGs), a medical monitoring technology recording cardiac activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is based on the analysis of the deformation of the signal shapes due to irregular heart rates associated with heart diseases. Due to the infeasibility of manual examination of large volumes of ECG data,
arxiv
Gene Transfer of Engineered Calmodulin Alleviates Ventricular Arrhythmias in a Calsequestrin-Associated Mouse Model of Catecholaminergic Polymorphic Ventricular Tachycardia [PDF]
, 2018 Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a familial arrhythmogenic syndrome characterized by sudden death. There are several genetic forms of CPVT associated with mutations in genes encoding the cardiac ryanodine receptor (RyR2 ...
Belevych, Andriy E +12 more
core +1 more source
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification [PDF]
arXiv, 2020 Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the
arxiv
An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram
IEEE Access, 2018 This paper presents an automatic wearable electrocardiogram (ECG) classification and monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device with wireless sensors to obtain the ECG data, and send these ECG data to a computer
Yufa Xia +6 more
semanticscholar +1 more source
Cardiac Arrhythmia and Geomagnetic Activity [PDF]
, 2006 Background: The purpose of this paper is a review of a number of studies considering links between life threatening cardiac arrhythmias, sudden cardiac death (SCD) and the level of environmental physical activity factors like geomagnetic activity (GMA)
Stoupel, E
core +2 more sources
Contact force sensing in ablation of ventricular arrhythmias using a 56-hole open-irrigation catheter: a propensity-matched analysis. [PDF]
, 2021 PURPOSE: The effect of adding contact force (CF) sensing to 56-hole tip irrigation in ventricular arrhythmia (VA) ablation has not been previously studied.
A Sarkozy +16 more
core +1 more source
Cardiac Adipose Tissue Segmentation via Image-Level Annotations [PDF]
arXiv, 2022 Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of cardiac tissue substrates, the treatment of complex arrhythmias such as atrial fibrillation and ventricular tachycardia can be further optimized by detecting arrhythmia ...
arxiv