# An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation

arXiv: https://arxiv.org/abs/2502.12836 · HTML: https://mlsanity.com/paper/2502.12836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 12 of 12)

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [Towards a Personal Health Large Language Model](https://mlsanity.com/paper/2406.06474/) (2024)
- [PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models](https://mlsanity.com/paper/2406.19283/) (2024)
- [MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis](https://mlsanity.com/paper/2408.07773/) (2024)
- [By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting](https://mlsanity.com/paper/2407.10385/) (2024)
- [ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models](https://mlsanity.com/paper/2311.12524/) (2023)
- [Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals](https://mlsanity.com/paper/2406.18069/) (2024)

## Built on by (most cited first, 4 of 4)

- [HEARTS: Benchmarking LLM Reasoning on Health Time Series](https://mlsanity.com/paper/2603.06638/) (2026)
- [COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion](https://mlsanity.com/paper/2605.15016/) (2026)
- [VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data](https://mlsanity.com/paper/2605.29483/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are revolutionizing healthcare by improving diagnosis, patient care, and decision support through interactive communication. More recently, they have been applied to analyzing physiological time-series like wearable data for health insight extraction. Existing methods embed raw numerical sequences directly into prompts, which exceeds token limits and increases computational costs. Additionally, some studies integrated features extracted from time-series in textual prompts or applied multimodal approaches. However, these methods often produce generic and unreliable outputs due to LLMs' limited analytical rigor and inefficiency in interpreting continuous waveforms. In this paper, we develop an LLM-powered agent for physiological time-series analysis aimed to bridge the gap in integrating LLMs with well-established analytical tools. Built on the OpenCHA, an open-source LLM-powered framework, our agent powered by OpenAI's GPT-3.5-turbo model features an orchestrator that integrates user interaction, data sources, and analytical tools to generate accurate health insights. To evaluate its effectiveness, we implement a case study on heart rate (HR) estimation from Photoplethysmogram (PPG) signals using a dataset of PPG and Electrocardiogram (ECG) recordings in a remote health monitoring study. The agent's performance is benchmarked against OpenAI GPT-4o-mini and GPT-4o, with ECG serving as the gold standard for HR estimation. Results demonstrate that our agent significantly outperforms benchmark models by achieving lower error rates and more reliable HR estimations. The agent implementation is publicly available on GitHub.