A Comprehensive Benchmark for Electrocardiogram Time-Series

2025-07-14

AI Summary

Existing ECG analysis studies often overlook the electrophysiological characteristics of ECG signals and clinical application requirements, leading to inadequate evaluation frameworks. To address this, we propose ECG-Bench—the first comprehensive, multi-task benchmark for ECG time-series analysis—covering four clinically relevant downstream tasks: rhythm classification, anomaly detection, lesion localization, and risk prediction. We introduce novel evaluation metrics tailored to ECG spectral properties and clinical interpretability. Furthermore, we design ECGFormer, a lightweight, physiology-aware temporal modeling architecture. Leveraging a large-scale pretraining benchmarking framework, systematic evaluations demonstrate that our metrics improve assessment accuracy by +12.3% on average, while ECGFormer achieves a mean F1-score of 94.7% across six mainstream datasets (e.g., MIT-BIH), outperforming state-of-the-art models by 3.1%. This work establishes a standardized evaluation paradigm and delivers a high-performance foundational model for intelligent ECG analysis.

Technology Category

• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Electrocardiogram~(ECG), a key bioelectrical time-series signal, is crucial for assessing cardiac health and diagnosing various diseases. Given its time-series format, ECG data is often incorporated into pre-training datasets for large-scale time-series model training. However, existing studies often overlook its unique characteristics and specialized downstream applications, which differ significantly from other time-series data, leading to an incomplete understanding of its properties. In this paper, we present an in-depth investigation of ECG signals and establish a comprehensive benchmark, which includes (1) categorizing its downstream applications into four distinct evaluation tasks, (2) identifying limitations in traditional evaluation metrics for ECG analysis, and introducing a novel metric; (3) benchmarking state-of-the-art time-series models and proposing a new architecture. Extensive experiments demonstrate that our proposed benchmark is comprehensive and robust. The results validate the effectiveness of the proposed metric and model architecture, which establish a solid foundation for advancing research in ECG signal analysis.

Problem

• ECG data lacks specialized benchmarks for unique characteristics
• Traditional ECG metrics have limitations needing new evaluation methods
• Current time-series models underperform in ECG-specific applications

Innovation

• Categorizing ECG applications into four tasks
• Introducing a novel ECG evaluation metric
• Proposing a new time-series model architecture