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Electrocardiography, Ambulatory

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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...

Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized...

Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition.

Sensors (Basel, Switzerland)
One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patien...

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while ...

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

Computer methods and programs in biomedicine
BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a si...

Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks.

IEEE transactions on neural networks and learning systems
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monit...

Novel application of convolutional neural networks for artificial intelligence-enabled modified moving average analysis of P-, R-, and T-wave alternans for detection of risk for atrial and ventricular arrhythmias.

Journal of electrocardiology
BACKGROUND: T-wave alternans (TWA) analysis was shown in >14,000 individuals studied worldwide over the past two decades to be a useful tool to assess risk for cardiovascular mortality and sudden arrhythmic death. TWA analysis by the modified moving ...

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...

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

Stress and health : journal of the International Society for the Investigation of Stress
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardi...

Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False-Positive Alerts.

Journal of the American Heart Association
BACKGROUND: Implantable loop recorders (ILRs) are increasingly placed for arrhythmia detection. However, historically, ≈75% of ILR alerts are false positives, requiring significant time and effort for adjudication. The LINQII and LUX-Dx are remotely ...