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[PDF][PDF] Arrhythmia modern classification techniques: A review
Artificial intelligence methods are utilized in biological signal processing to locate and
extract interesting data. The examination of ECG signal characteristics is crucial for the …
extract interesting data. The examination of ECG signal characteristics is crucial for the …
[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
Will two do? Varying dimensions in electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021
The PhysioNet/Computing in Cardiology Challenge 2021 focused on the identification of
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …
cardiac abnormalities from electrocardiograms (ECGs) and assessed the diagnostic …
Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement
Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac
arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) …
arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) …
Arrhythmia classification techniques using deep neural network
Electrocardiogram (ECG) is the most common and low‐cost diagnostic tool used in
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …
Maefe: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning
Electrocardiogram (ECG) is a universal diagnostic tool for heart disease, which can provide
data for deep learning. The scarcity of labeled data is a major challenge for medical artificial …
data for deep learning. The scarcity of labeled data is a major challenge for medical artificial …
Machine learning for real-time heart disease prediction
Heart-related anomalies are among the most common causes of death worldwide. Patients
are often asymptomatic until a fatal event happens, and even when they are under …
are often asymptomatic until a fatal event happens, and even when they are under …
Bolstering the secretion and bioactivities of umbilical cord MSC-derived extracellular vesicles with 3D culture and priming in chemically defined media
Human mesenchymal stem cells (hMSCs)-derived extracellular vesicles (EVs) have been
known to possess the features of the origin cell with nano size and have shown therapeutic …
known to possess the features of the origin cell with nano size and have shown therapeutic …
Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records
Background and objective Arrhythmia constitute a common clinical problem in cardiology.
The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG …
The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG …
Issues in the automated classification of multilead ECGs using heterogeneous labels and populations
Objective. The standard twelve-lead electrocardiogram (ECG) is a widely used tool for
monitoring cardiac function and diagnosing cardiac disorders. The development of smaller …
monitoring cardiac function and diagnosing cardiac disorders. The development of smaller …