Training neural networks with synthetic electrocardiograms
This method enables training neural networks with free, accurately labeled synthetic data, eliminating manual annotation and class imbalances, which is beneficial for healthcare applications, though it is incremental as it builds on existing domain randomization techniques.
The authors tackled the problem of training neural networks for ECG analysis by using synthetic data with domain randomization, achieving performance on par with or better than models trained on real data for r-wave detection across various physical activities and atrial fibrillation conditions.
We present a method for training neural networks with synthetic electrocardiograms that mimic signals produced by a wearable single lead electrocardiogram monitor. We use domain randomization where the synthetic signal properties such as the waveform shape, RR-intervals and noise are varied for every training example. Models trained with synthetic data are compared to their counterparts trained with real data. Detection of r-waves in electrocardiograms recorded during different physical activities and in atrial fibrillation is used to compare the models. 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. Experiments show robust performance with different seeds and training examples on different test sets without any test set specific tuning. The method makes possible to train neural networks using practically free-to-collect data with accurate labels without the need for manual annotations and it opens up the possibility of extending the use of synthetic data on cardiac disease classification when disease specific a priori information is used in the electrocardiogram generation. Additionally the distribution of data can be controlled eliminating class imbalances that are typically observed in health related data and additionally the generated data is inherently private.