Holtercare-Bench: A Multimodal Benchmark for Evaluating Long-Term Dynamic ECG Analysis
Holtercare-Bench is a novel multimodal benchmark designed to evaluate long-term dynamic ECG analysis in medical MLLMs Holtercare-23K dataset introduces 22,980 QA pairs from 788 clinical Holter records with signal-video-text tri-modal alignment Zero-shot evaluations reveal significant performance gaps in leading MLLMs when processing ultra-long pathological ECG sequences Fine-tuning representative models yields substantial improvements, demonstrating the benchmark's utility for model development
Analysis
TL;DR
- Holtercare-Bench is a novel multimodal benchmark designed to evaluate long-term dynamic ECG analysis in medical MLLMs
- Holtercare-23K dataset introduces 22,980 QA pairs from 788 clinical Holter records with signal-video-text tri-modal alignment
- Zero-shot evaluations reveal significant performance gaps in leading MLLMs when processing ultra-long pathological ECG sequences
- Fine-tuning representative models yields substantial improvements, demonstrating the benchmark's utility for model development
- The work establishes a foundational benchmark for advancing long-term medical MLLMs in electrophysiology
Why It Matters
This benchmark addresses a critical gap in medical AI where most MLLMs are optimized for static images or short-term signals, leaving long-term dynamic ECG analysis severely underexplored. For AI practitioners working in healthcare, it provides both a rigorous evaluation framework and a dataset that reflects real-world clinical complexity in cardiac monitoring.
Technical Details
- Holtercare-23K Dataset: 22,980 QA pairs derived from 788 clinical Holter records, featuring a novel signal-video-text tri-modal alignment that bridges raw ECG signals, visual representations, and clinical text
- Holtercare-Bench Evaluation Suite: Three core evaluation dimensions—temporal localization (identifying pathological events within long sequences), clinical diagnosis (accurate condition classification), and global summarization (comprehensive report generation)
- Zero-shot MLLM Evaluation: Leading multimodal large language models were tested without fine-tuning, revealing significant performance deficits on ultra-long pathological ECG sequences
- Fine-tuning Results: Representative models showed substantial performance gains after fine-tuning on Holtercare-23K, validating the dataset's effectiveness for downstream task adaptation
- arXiv Reference: 2608.19297, submitted August 19, 2026, in the Machine Learning (cs.LG) domain
Industry Insight
- The performance gap in zero-shot evaluation signals that current MLLMs are not yet ready for deployment in long-term cardiac monitoring without task-specific fine-tuning, urging healthcare AI developers to invest in temporal reasoning capabilities
- The signal-video-text tri-modal alignment approach could serve as a blueprint for other medical domains requiring integration of time-series signals with clinical narratives
- As Holtercare-Bench becomes a standard evaluation tool, it will likely accelerate the development of specialized medical MLLMs and create competitive pressure for models to demonstrate proficiency in temporal medical signal understanding
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