PyPulse: A Python Library for Biosignal Imputation (2412.06382v1)

Abstract: 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.

• The paper introduces PyPulse, a flexible Python library with a modular design and 11 diverse algorithms for imputing missing data in biosignals.
• PyPulse supports custom datasets and missingness mechanisms through flexible configurations and offers interactive visualization tools to compare methods.
• The library facilitates research into novel imputation strategies, enhancing data reliability for health monitoring and remote healthcare applications.

Overview of PyPulse: A Python Library for Biosignal Imputation

The paper presents PyPulse, a Python library designed to address challenges in biosignal imputation, particularly in clinical and wearable sensor environments where data loss is prevalent. PyPulse offers a comprehensive toolkit that facilitates imputation tasks, significant given the susceptibility of biosignals to missing data due to factors such as sensor displacement and data transmission issues.

Primary Contributions:

1. Modular and Extendable Architecture: PyPulse is designed with a highly modular architecture to ensure flexibility and extensibility. Its design reflects the need to adapt to various biosignal datasets and imputation configurations. The software is structured with distinct modules for experimentation, datasets, models, and visualization, all managed by YAML configuration files, which assist in simplifying the experimental setup.
2. Support for Custom Datasets and Missingness Mechanisms: The library supports user-provided custom datasets, offering the ability to choose or define missingness mechanisms without modifying the core architecture. This is achieved via flexible class hierarchies and configuration files, enabling users to map missingness strategies to corresponding implementation classes simply by extending the base classes.
3. Diverse Imputation Techniques: PyPulse incorporates 11 imputation algorithms spanning both classical methods (e.g., linear interpolation, mean filling, FFT) and state-of-the-art deep learning methods (e.g., BDC Transformer, DeepMVI, Vanilla Transformer, RNN-GAN). This range ensures that users can select appropriate methods suited to their specific datasets and objectives.
4. Interactive Visualization Tools: The tool includes an interactive visualization module, enhancing the user experience and supporting comprehensive comparisons between different imputation methods. This interface is particularly valuable for visual assessment of imputation results against ground truth, facilitating insights into model performance.
5. Ease of Use and Efficiency: The framework enables users to configure and execute imputation tasks using single command-line instructions. Such ease of use is critical for facilitating experimentation and rapid iteration, especially for researchers unfamiliar with machine learning environments.

Implications and Future Directions:

The PyPulse library provides a robust foundation for biosignal imputation, promoting cross-disciplinary research opportunities. It allows for the development and testing of novel imputation strategies without requiring extensive adjustments to the codebase. This capability is pivotal in fostering the application of machine learning techniques to health-related time-series data, particularly as wearable sensor technology becomes more ubiquitous and integral to remote healthcare monitoring.

From a theoretical perspective, PyPulse facilitates the exploration of imputation methodologies tailored to the specific quirks of biosignal data, such as quasi-periodicity and morphological features. This can deepen understanding and lead to improved methodologies for handling missing data in biosignals. Practically, the application of diverse imputation models through PyPulse can enhance data reliability and accuracy, directly impacting clinical outcomes and health monitoring systems.

In the field of AI, future developments could explore further integration of cutting-edge machine learning models, such as advancements in transformer architectures or the employment of generative models for imputation. Additionally, the adaptability of PyPulse's framework suggests potential for expansion into broader domains where time-series data is prevalent, facilitating a more unified approach to imputation across different fields of paper.