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

• URL: http://arxiv.org/abs/2603.03331v1
• Date: Tue, 10 Feb 2026 18:46:03 GMT
• Title: PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning
• Abstract summary: Photoplethysmography is a widely used non-invasive sensing modality for continuous cardiovascular and physiological monitoring.<n>Existing PPG datasets provide supervision in the form of numerical measurements or task-specific labels.<n>We introduce PulseLM, a large-scale PPG-text dataset designed to bridge raw PPG waveforms and natural language.
• Score: 22.000303329807462
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• A robust PPG foundation model using multimodal physiological supervision [7.441186015526675]
  Photoplethysmography is a non-invasive measure of changes in blood volume.<n>Recent PPG foundation models either use open-source ICU datasets with pretraining paradigms that require curated data, or use closed-source field-like PPG data.<n>We propose a PPG foundation model that does not require high-quality or field-like pretraining data, and instead leverages accompanying electrocardiogram and respiratory signals in ICU datasets to select contrastive samples during pretraining.
  arXiv  Detail & Related papers  (2026-06-05T15:08:50Z)
• Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG [2.2945816941393766]
  Photoplethysmography (signals) is one of the most widely captured biosignals for clinical prediction tasks.<n>Photoplethysmography (signals) is one of the most widely captured biosignals for clinical prediction tasks.
  arXiv  Detail & Related papers  (2026-03-23T11:19:06Z)
• Wavelet-Driven Masked Multiscale Reconstruction for PPG Foundation Models [13.267230682892503]
  Masked Multiscale Reconstruction (MMR) is a self-supervised pretraining framework that explicitly learns from hierarchical time-frequency scales of PPG data.<n>We pretrain our model with MMR using 17 million unlabeled 10-second PPG segments from 32,000 users.<n>On 17 of 19 diverse health-related tasks, MMR trained on large-scale wearable PPG data improves over or matches state-of-the-art open-source PPG models.
  arXiv  Detail & Related papers  (2026-01-18T01:34:47Z)
• MedQARo: A Large-Scale Benchmark for Medical Question Answering in Romanian [50.767415194856135]
  We introduce MedQARo, the first large-scale medical QA benchmark in Romanian.<n>We construct a high-quality and large-scale dataset comprising 102,646 QA pairs related to cancer patients.
  arXiv  Detail & Related papers  (2025-08-22T13:48:37Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [46.36100528165335]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings [15.077834063212704]
  Photoplethysmography-based foundation models are gaining traction due to their potential to generalize across diverse health applications.<n>Existing PPG foundation models are either open-source but trained on clinical data or closed-source, limiting their applicability in real-world settings.<n>We introduce Pulse, the first open-source PPG foundation model trained exclusively on raw PPG data collected over a 100-day field study with 120 participants.
  arXiv  Detail & Related papers  (2025-02-03T06:56:40Z)
• HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation [89.3260120072177]
  We propose a novel Historical-Constrained Large Language Models (HC-LLM) framework for Radiology report generation.<n>Our approach extracts both time-shared and time-specific features from longitudinal chest X-rays and diagnostic reports to capture disease progression.<n> Notably, our approach performs well even without historical data during testing and can be easily adapted to other multimodal large models.
  arXiv  Detail & Related papers  (2024-12-15T06:04:16Z)
• PaPaGei: Open Foundation Models for Optical Physiological Signals [8.78925327256804]
  Photoplethysmography is the leading non-invasive technique for monitoring biosignals and cardiovascular health.<n>Machine learning models trained on PPG signals tend to be task-specific and struggle with generalization.<n>We present PaPaGei, the first open foundation model for PPG signals.
  arXiv  Detail & Related papers  (2024-10-27T18:18:06Z)
• Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models [76.52997424694767]
  Keyphrase Generation (KPG) is a longstanding task in NLP with widespread applications. Seq2seq pre-trained language models (PLMs) have ushered in a transformative era for KPG, yielding promising performance improvements. This paper undertakes a systematic analysis of the influence of model selection and decoding strategies on PLM-based KPG.
  arXiv  Detail & Related papers  (2023-10-10T07:34:45Z)
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
  Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
  arXiv  Detail & Related papers  (2023-04-27T18:29:05Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.