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Found 4 papers, 1 papers with code
Flexible framework for generating synthetic electrocardiograms and photoplethysmograms
For example, we trained an LSTM to detect ECG R-peaks using both real ECG signals from the MIT-BIH arrythmia set and our new generator.
Empirical investigation of multi-source cross-validation in clinical machine learning
However, such estimates tend to be highly overoptimistic when compared to accuracy obtained from deploying models to sources not represented in the dataset, such as a new hospital.
Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study
We then compared the personalized models against a generalized model trained using data from all 10 participants.
Training neural networks with synthetic electrocardiograms
By allowing the randomization to increase beyond what is typically observed in the real-world data the performance is on par or superseding the performance of networks trained with real data.
