统一工具包解决生理信号分析中的数据混乱与实验不可复现问题
Tyee: A Unified, Modular, and Fully-Integrated Configurable Toolkit for Intelligent Physiological Health Care
- 支持12种信号模态的统一接口与可配置预处理流程
- 在13个数据集上12个达顶尖水平，全任务表现稳定优越
- 适合医疗AI研究者快速搭建可复现的生理信号分析系统
深度学习在生理信号分析中展现巨大潜力，但受限于数据格式不一、预处理策略不一致、模型流程碎片化及实验设置不可复现。为此，我们提出Tyee——一个统一、模块化且完全集成的可配置工具包，专为智能生理健康应用设计。其三大创新包括：(1) 支持12类信号模态的统一数据接口与可配置预处理流水线；(2) 模块化可扩展架构，支持跨任务灵活集成与快速原型开发；(3) 端到端工作流配置，促进可复现与可扩展的实验。Tyee在所有评估任务中均表现出一致的有效性与泛化能力，在13个数据集中有12个达到或超过基线（其中12个为最优），性能领先。工具包已开源并持续维护。
原文摘要 · Abstract (English)
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.
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