PyPulse: A Python Library for Biosignal Imputation
This work addresses a practical issue for bioresearchers and practitioners dealing with incomplete biosignal data, though it is incremental as it focuses on packaging existing methods into a more accessible tool.
The authors tackled the problem of missing data in biosignals from clinical and wearable sensors by introducing PyPulse, a Python library that provides a modular and user-friendly framework for imputation, enabling non-machine-learning researchers to easily apply pre-trained methods and compare baselines with minimal code.
We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in these settings and can arise from multiple causes, such as insecure sensor attachment or data transmission loss. PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad userbase, including non-machine-learning bioresearchers. Specifically, its new capabilities include using pre-trained imputation methods out-of-the-box on custom datasets, running the full workflow of training or testing a baseline method with a single line of code, and comparing baseline methods in an interactive visualization tool. We released PyPulse under the MIT License on Github and PyPI. The source code can be found at: https://github.com/rehg-lab/pulseimpute.