Story
Position: Evaluation of ECG Representations Must Be Fixed
Key takeaway
Researchers say current methods for evaluating AI algorithms on ECG data are flawed, risking unreliable progress that may not translate to real-world clinical benefit.
Quick Explainer
The core idea is that the current practice of evaluating ECG representation learning models using standard arrhythmia and waveform classification tasks fails to capture the broader clinical utility of the ECG signal. The authors propose expanding the set of downstream evaluation tasks to include clinically relevant objectives like structural heart disease diagnosis, hemodynamic state inference, and patient-level forecasting. They also highlight issues with the field's reliance on macro-AUROC as the primary performance metric, which can mask important differences in per-task performance, especially on imbalanced medical datasets. Notably, the authors find that a randomly initialized encoder often performs competitively with specialized pre-training methods, suggesting the ECG signal may be so information-rich that additional pre-training provides limited benefit for many tasks.
Deep Dive
Technical Deep Dive: Position Paper on ECG Representation Evaluation
Overview
This technical deep dive summarizes the key points from a position paper arguing that the current benchmarking practice for evaluating 12-lead ECG representation learning models must be improved. The paper outlines several limitations of the field's reliance on standard multi-label arrhythmia and waveform classification datasets, and proposes expanding the set of downstream evaluation tasks to better reflect the broader clinical utility of the ECG. The authors also identify issues with the field's heavy use of macro-AUROC as the primary performance metric, and provide recommendations for more rigorous and informative evaluation protocols.
Problem & Context
- 12-lead ECG representation learning is an active area of research, with many new methods proposed in major ML venues
- The field has largely converged on three public multi-label benchmarks (PTB-XL, CPSC2018, CSN) focused primarily on arrhythmia and waveform-morphology classification tasks
- However, the ECG is known to encode broader clinical information beyond just arrhythmia, such as structural heart disease and patient-level forecasting
- Evaluation practices in the field often lack rigor, with heavy reliance on macro-AUROC and insufficient reporting of uncertainty and per-task performance
Methodology
- The authors survey 28 ECG representation learning papers published since 2019 to understand the field's current benchmarking practices
- They propose expanding the set of downstream evaluation tasks to include:
- Structural heart disease diagnosis
- Hemodynamic state inference (e.g., pulmonary capillary wedge pressure)
- Patient-level forecasting (e.g., risk of heart failure)
- They also outline a set of evaluation best practices, including:
- Reporting per-task AUROC and AUPRC, not just macro-AUROC
- Quantifying uncertainty with confidence intervals
- Excluding tasks with insufficient test set support
Data & Experimental Setup
- For their empirical study, the authors evaluate three representative ECG pre-training methods (CLOCS, MERL, D-BETA) on six downstream tasks:
- The three standard public benchmarks (PTB-XL, CPSC2018, CSN)
- A structural heart disease dataset (EchoNext)
- Hemodynamic inference tasks (mPCWP, mPA)
- A patient forecasting task (1-year heart failure risk)
Results
- Applying the proposed evaluation best practices can change the perceived relative performance of ECG representation learning methods:
- On the standard benchmarks, a randomly initialized encoder is often competitive with or exceeds specialized pre-training methods like CLOCS
- Performance differences between MERL and D-BETA frequently fall within sampling noise
- On structural heart disease and patient forecasting tasks, MERL and D-BETA outperform CLOCS, but the random baseline remains a strong competitor
- For hemodynamic inference, the methods perform within statistical parity of each other
Interpretation
- The authors argue that current benchmarking practices overly emphasize arrhythmia and waveform tasks, missing the broader clinical utility of the ECG
- Macro-AUROC can mask important differences in per-task performance, especially on imbalanced datasets common in medical applications
- A randomly initialized encoder performing competitively suggests the ECG signal may be so information-rich that specialized pre-training provides limited additional benefit for many tasks
Limitations & Uncertainties
- The analysis is limited to three representative pre-training methods, while the broader literature contains many more proposed approaches
- The authors only evaluate on a subset of the proposed downstream tasks, due to data access constraints
- Performance on the private datasets (hemodynamic inference, patient forecasting) may not generalize to other institutional settings
What Comes Next
- The authors call for the community to agree on a standardized set of clinically relevant ECG evaluation tasks
- They propose an open-source framework to enable rigorous, reproducible benchmarking of ECG representation learning methods
- Future work should explore why a randomly initialized encoder performs so well, and investigate multi-modal approaches that combine ECG with other data modalities