Tyee: A Unified, Modular, and Fully-Integrated Configurable Toolkit for Intelligent Physiological Health Care

TL;DR

Tyee is a comprehensive toolkit that streamlines physiological signal analysis through unified data handling, modular design, and reproducible workflows, significantly advancing research and application in healthcare diagnostics.

Contribution

It introduces a fully integrated, configurable platform that supports multiple signal types, flexible model development, and reproducible experiments, addressing key challenges in physiological data analysis.

Findings

Outperforms baselines on 12 of 13 datasets

Demonstrates high generalizability across tasks

Enables rapid prototyping and reproducibility

Abstract

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.