Exercise-related palpitations, vertigo, and syncope may be caused by benign etiologies but can also herald life-threatening arrhythmias. The precise diagnosis of these findings is therefore essential and potentially life saving but often is a challenge for sports physicians and cardiologists. Leadless, ambulatory HR monitors with chest strap transmitters have been designed to steer exercise intensity in athletes with a baseline sinus rhythm. However, they also can capture arrhythmias. Presented here are three cases of varying arrhythmias: atrial fibrillation, atrioventricular nodal reentrant tachycardia, and ectopic atrial tachycardia that demonstrate the utility of HR monitors designed for athletic purposes in facilitating the diagnosis of arrhythmias and guiding appropriate treatment.
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http://dx.doi.org/10.1249/MSS.0b013e31828ca1bf | DOI Listing |
Sensors (Basel)
December 2024
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), University of Utah School of Medicine, 95 S 2000 E, Salt Lake City, UT 84112, USA.
Heart failure with preserved ejection fraction (HFpEF) is increasing at an alarming rate worldwide, with limited effective therapeutic interventions in patients. Sudden cardiac death (SCD) and ventricular arrhythmias present substantial risks for the prognosis of these patients. Obesity is a risk factor for HFpEF and life-threatening arrhythmias.
View Article and Find Full Text PDFMedicina (Kaunas)
December 2024
Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, 737 N. Michigan Avenue Suite 1600, Chicago, IL 60611, USA.
: Studies have shown that inflammation markers can be used as prognostic tools in predicting acute ischemic stroke. In this study, we conducted a comparison of several inflammation scores in predicting left atrial thrombosis (LAT) in patients with ischemic stroke without AF. : In this single-center, retrospective study, we included 303 consecutive patients with ischemic stroke.
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