CAMEL: An ECG Language Model for Forecasting Cardiac Events

TL;DR

CAMEL is a novel ECG language model capable of forecasting future cardiac events by understanding ECG signals and text, demonstrating state-of-the-art zero-shot performance across multiple datasets and benchmarks.

Contribution

This work introduces CAMEL, the first ECG language model with forecasting ability, trained with a specialized encoder and curriculum learning for improved clinical prediction.

Findings

Achieves state-of-the-art results on ECGForecastBench and ECGBench.

Demonstrates strong zero-shot performance across diverse datasets.

Outperforms fully supervised models in forecasting accuracy.

Abstract

Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a promising framework for ECG classification accompanied by report generation. However, current models cannot forecast future cardiac events despite the immense clinical value for planning earlier intervention. To address this gap, we propose CAMEL, the first ELM that is capable of inference over longer signal durations which enables its forecasting capability. Our key insight is a specialized ECG encoder which enables cross-understanding of ECG signals with text. We train CAMEL using established LLM training procedures, combining LoRA adaptation with a curriculum learning pipeline. Our curriculum includes ECG classification, metrics calculations, and multi-turn conversations to elicit reasoning. CAMEL demonstrates strong zero-shot performance across 6 tasks and 9 datasets, including ECGForecastBench, a new benchmark that we introduce for forecasting arrhythmias. CAMEL is on par with or surpasses ELMs and fully supervised baselines both in- and out-of-distribution, achieving SOTA results on ECGBench (+7.0% absolute average gain) as well as ECGForecastBench (+12.4% over fully supervised models and +21.1% over zero-shot ELMs).