The experiments showed that self-supervised pre-training relying on negative sample pairs could achieve significantly better ECG representation than baseline, ...
The experiments showed that self-supervised pre-training relying on negative sample pairs could achieve significantly better ECG representation than baseline.
Oct 2, 2024 · In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data.
We propose a self-supervised learning method for 12-lead electrocardiograms (ECGs). For pretraining the model we design a task to mask out subsegements of all ...
Aug 30, 2024 · Among these methods, the electrocardiogram (ECG) remains a pivotal tool, offering critical insights into the heart's electrical activity.
WildECG: Ubiquitous ECG Pre-Training. This repo contains code implementation as well as trained models for ECG data analysis.
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Mar 30, 2024 · Pretraining with a self-supervised approach like VICReg addresses labeled data constraints in rare cardiac diseases, enabling the learning of ...
Sep 25, 2024 · This study proposed a method to incorporate multiple pretraining tasks for better representation learning via utilizing the [CLS] token more ...
Here, we propose a self-supervised learning method for 12-lead electrocardiograms (ECGs). For pretraining the model we design a task to mask out subsegements of ...
Oct 22, 2024 · ... Self-supervised learning has proved effective in classifying raw ECG signals into abnormality classes despite scarce expert-provided labels.