PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning
arXiv:2603.03331v1 Announce Type: new Abstract: 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 suitability for language-based physiological reasoning and multimodal foundation models. In this work, we introduce PulseLM, a large-scale PPG-text dataset designed to bridge raw PPG waveforms and natural language through a unified, closed-ended question answering (QA) formulation. PulseLM aggregates PPG recordings from fifteen publicly available sources and harmonizes heterogeneous annotations into twelve common physiologically QA tasks. The dataset comprises 1.31 million standardized 10-second PPG segments, associated with 3.15 million question-answer pairs. We further defi
arXiv:2603.03331v1 Announce Type: new Abstract: 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 suitability for language-based physiological reasoning and multimodal foundation models. In this work, we introduce PulseLM, a large-scale PPG-text dataset designed to bridge raw PPG waveforms and natural language through a unified, closed-ended question answering (QA) formulation. PulseLM aggregates PPG recordings from fifteen publicly available sources and harmonizes heterogeneous annotations into twelve common physiologically QA tasks. The dataset comprises 1.31 million standardized 10-second PPG segments, associated with 3.15 million question-answer pairs. We further define reproducible preprocessing, supervision, and evaluation protocols and establish baseline benchmarks using multimodal PPG-aware large language models. PulseLM provides a standardized foundation for studying multimodal physiological reasoning, cross-dataset generalization, and scalable benchmarking of PPG-based language models. The data and code can be found publicly available at: https://github.com/manhph2211/PulseLM.
Executive Summary
The article 'PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning' introduces PulseLM, a large-scale dataset designed to bridge raw photoplethysmography (PPG) waveforms and natural language through a unified question-answering (QA) formulation. The dataset aggregates PPG recordings from 15 public sources, harmonizes annotations, and provides 1.31 million standardized 10-second PPG segments associated with 3.15 million QA pairs. The authors establish reproducible protocols and baseline benchmarks using multimodal PPG-aware large language models. PulseLM serves as a standardized foundation for studying multimodal physiological reasoning, cross-dataset generalization, and scalable benchmarking of PPG-based language models. The dataset and code are publicly available, facilitating further research and development in this area. This contribution has the potential to advance the field of PPG-text learning and multimodal foundation models.
Key Points
- ▸ PulseLM is a large-scale dataset designed to bridge raw PPG waveforms and natural language
- ▸ The dataset aggregates PPG recordings from 15 public sources and harmonizes annotations
- ▸ The authors establish reproducible protocols and baseline benchmarks using multimodal PPG-aware large language models
Merits
Strength in Interdisciplinary Approach
The authors effectively integrate expertise from both PPG and natural language processing (NLP) fields, demonstrating a comprehensive understanding of the challenges and opportunities in PPG-text learning.
Methodological Rigor
The authors provide clear, step-by-step protocols for data preprocessing, supervision, and evaluation, ensuring reproducibility and facilitating further research.
Potential for Scalable Benchmarking
PulseLM has the potential to serve as a standardized benchmark for evaluating PPG-based language models, enabling researchers to compare and improve their approaches.
Demerits
Limited Contextual Information
The article does not provide explicit information on the contextual factors that may influence PPG waveforms, which could be relevant for developing more effective PPG-text learning models.
Dependence on Publicly Available Data
The authors rely on 15 public sources for their dataset, which may limit the diversity and representativeness of the data, potentially impacting the generalizability of their findings.
Expert Commentary
The article makes a significant contribution to the field of PPG-text learning by introducing a large-scale dataset and establishing reproducible protocols and baseline benchmarks. The authors' interdisciplinary approach and methodological rigor demonstrate a comprehensive understanding of the challenges and opportunities in this area. However, the limited contextual information and dependence on publicly available data may impact the generalizability of their findings. To further advance the field, researchers should explore ways to integrate contextual factors and develop more diverse and representative datasets. Additionally, policymakers and industry stakeholders should consider the potential implications of PPG-text learning technologies on healthcare and society.
Recommendations
- ✓ Researchers should prioritize developing more diverse and representative datasets for PPG-text learning tasks, including contextual information and a broader range of PPG waveforms and annotations.
- ✓ Industry stakeholders and policymakers should consider the potential implications of PPG-text learning technologies on healthcare and society, including issues related to data privacy, security, and standardization.