Tyee: A Unified, Modular, and Fully-Integrated Configurable Toolkit for Intelligent Physiological Health Care
This toolkit addresses reproducibility and scalability issues for researchers and practitioners in intelligent physiological healthcare, representing an incremental improvement by integrating existing methods into a more efficient framework.
The paper tackles the challenges of heterogeneous data formats, inconsistent preprocessing, and fragmented pipelines in deep learning for physiological signal analysis by introducing Tyee, a unified and modular toolkit that outperforms or matches baselines across all evaluated tasks, achieving state-of-the-art results on 12 of 13 datasets.
Deep learning has shown great promise in physiological signal analysis, yet its progress is hindered by heterogeneous data formats, inconsistent preprocessing strategies, fragmented model pipelines, and non-reproducible experimental setups. To address these limitations, we present Tyee, a unified, modular, and fully-integrated configurable toolkit designed for intelligent physiological healthcare. Tyee introduces three key innovations: (1) a unified data interface and configurable preprocessing pipeline for 12 kinds of signal modalities; (2) a modular and extensible architecture enabling flexible integration and rapid prototyping across tasks; and (3) end-to-end workflow configuration, promoting reproducible and scalable experimentation. Tyee demonstrates consistent practical effectiveness and generalizability, outperforming or matching baselines across all evaluated tasks (with state-of-the-art results on 12 of 13 datasets). The Tyee toolkit is released at https://github.com/SmileHnu/Tyee and actively maintained.