arXiv 2603.033312026-05-07cs.CLcs.AI

PulseLM：PPG-文本学习的基础数据集和基准

PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning

Singapore Management University（新加坡管理大学）；Queen Mary University of London（伦敦玛丽女王大学）；Eindhoven University of Technology（埃因霍温理工大学）；University of Cambridge（剑桥大学）

Hung Manh Pham, Jinyang Wu, Xiao Ma, Yiming Zhang, Yixin Xu, Aaqib Saeed, Bin Zhu, Zhou Pan, Dong Ma

备注：: PulseLM v2

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AI总结：

PulseLM通过统一的问答框架连接原始PPG波形与自然语言，包含100万标准化PPG片段及250万问题-答案对，为研究语言基础生理推断和多模态模型基准提供标准化基础。

摘要：

脉冲波形图（PPG）是一种广泛使用的非侵入式传感模态，用于连续的心血管和生理监测，在临床、实验室和可穿戴设备中均有应用。尽管现有PPG数据集支持广泛的任务，但它们通常以数值测量或任务特定标签的形式提供监督，限制了其与基于语言的接口和多模态基础模型的兼容性。在本工作中，我们介绍了PulseLM，一个大规模的PPG-文本问答数据集，通过统一的问答（QA）框架将原始PPG波形与自然语言连接起来。PulseLM整合了十六个公开可用的数据源中的PPG记录，并将异构标注统一到12个下游任务中。该数据集包含超过100万个标准化的10秒PPG片段，与近250万个问题-答案对相关联。我们进一步定义了可重复的数据管道、训练和评估协议，并通过多模态PPG-aware大语言模型建立了基线基准。PulseLM为研究语言基础生理推断、跨数据集泛化和PPG多模态模型的可扩展基准提供了标准化基础。我们公开发布了数据集和代码，分别在https://huggingface.co/datasets/Manhph2211/PulseLM和https://github.com/manhph2211/PULSE-LM上。

英文摘要：

Photoplethysmography (PPG) is a widely used non-invasive sensing modality for continuous cardiovascular and physiological monitoring across clinical, laboratory, and wearable settings. While existing PPG datasets support a broad range of downstream tasks, they typically provide supervision in the form of numerical measurements or task-specific labels, limiting their compatibility with language-based interfaces and multimodal foundation models. In this work, we introduce PulseLM, a large-scale PPG-text question-answering dataset that bridges raw PPG waveforms and natural language through a unified question-answering (QA) formulation. PulseLM aggregates PPG recordings from sixteen publicly available sources and harmonizes heterogeneous annotations into 12 downstream tasks. The dataset comprises over 1 million standardized 10-second PPG segments, associated with nearly 2.5 million question-answer pairs. We further define reproducible data pipeline, training, and evaluation protocols and establish baseline benchmarks using multimodal PPG-aware large language models. PulseLM provides a standardized foundation for studying language-grounded physiological inference, cross-dataset generalization, and scalable benchmarking of PPG-based multimodal models. We publicly release the dataset and code at https://huggingface.co/datasets/Manhph2211/PulseLM and https://github.com/manhph2211/PULSE-LM, respectively.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。