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Found 4 papers, 0 papers with code
SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation
An electrocardiogram (ECG) is vital for identifying cardiac diseases, offering crucial insights for diagnosing heart conditions and informing potentially life-saving treatments.
Self-Trained Model for ECG Complex Delineation
We conduct experiments demonstrating that our dataset is a valuable resource for training robust models and that our proposed self-trained method improves the prediction quality of ECG delineation.
Local Methods with Adaptivity via Scaling
A widely used and extensively researched technique to mitigate the communication bottleneck involves performing local training before communication.
Deep Neural Networks Generalization and Fine-Tuning for 12-lead ECG Classification
In this paper, we propose a methodology to improve the quality of heart disease prediction regardless of the dataset by training neural networks on a variety of datasets with further fine-tuning for the specific dataset.
