Machine Learning Workflow For Edge Computed Arrhythmia Detection In Exploration Class Missions

This article discusses the use of predictive models and edge computing for preventative care in deep-space missions. The authors demonstrate an innovative approach using Open Neural Network Exchange (ONNX) formats and self-optimizing training to classify heart rhythms in electrocardiogram (ECG) recordings. The results show high accuracy and efficiency in tachycardia detection, making this method suitable for deployment on wearable edge devices.