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Found 6 papers, 5 papers with code
Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata
These components consistently enhance performance beyond the existing state-of-the-art, which is predominantly based on convolutional models.
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction.
ECG Feature Importance Rankings: Cardiologists vs. Algorithms
Feature importance methods promise to provide a ranking of features according to importance for a given classification task.
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures.
Self-supervised representation learning from 12-lead ECG data
In a first step, we learn contrastive representations and evaluate their quality based on linear evaluation performance on a recently established, comprehensive, clinical ECG classification task.
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training
In this work we propose a method for reducing the computational cost of backprop, which we named dithered backprop.
