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Electrocardiography

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A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.

Computers in biology and medicine
The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm over...

CLINet: A novel deep learning network for ECG signal classification.

Journal of electrocardiology
Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this pape...

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

Heart rhythm
BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic r...

A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
BACKGROUND: Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.

A novel deep learning approach for early detection of cardiovascular diseases from ECG signals.

Medical engineering & physics
Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical diagnosis. Despite individuals' normal outward appearance and routine activities, subtle indications of these diseases can manifest in the electrocardio...

Towards federated transfer learning in electrocardiogram signal analysis.

Computers in biology and medicine
Modern methods in artificial intelligence perform very well on many healthcare datasets, at times outperforming trained doctors. However, many assumptions made in model training are not justifiable in clinical settings. In this work, we propose a met...

Deep learning-based diagnosis of aortic dissection using an electrocardiogram: Development, validation, and clinical implications of the AADE score.

Kardiologia polska
BACKGROUND: Aortic dissection (AD) is frequently associated with abnormalities in electrocardiographic findings. Advancements in medical technology present an opportunity to leverage these observations to improve patient diagnosis and care.

Heart failure classification using deep learning to extract spatiotemporal features from ECG.

BMC medical informatics and decision making
BACKGROUND: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal...