AIMC Topic: Electrocardiography

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Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: The increasing use of insertable cardiac monitors (ICM) produces a high rate of false positive (FP) diagnoses. Their verification results in a high workload for caregivers. We evaluated the performance of an artificial intelligence (AI)-based I...

Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.

JAMA cardiology
IMPORTANCE: Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular di...

Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study.

Annals of medicine
Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could benefit from the establishment of genetic probability prior to the test. This study aimed to develop a simple tool to provide genotype prediction for H...

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval fro...

Memory Classifiers for Robust ECG Classification against Physiological Noise.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The development of sophisticated machine learning algorithms has made it possible to detect critical health conditions like cardiac arrhythmia, directly from electrocardiogram (ECG) recordings. Large-scale machine learning models, like deep neural ne...

Beatwise ECG Classification for the Detection of Atrial Fibrillation with Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Atrial fibrillation (AF) is the most common, sustained cardiac arrhythmia. Early intervention and treatment could have a much higher chance of reversing AF. An electrocardiogram (ECG) is widely used to check the heart's rhythm and electrical activity...

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity...

Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automa...