A Comprehensive Benchmark for Electrocardiogram Time-Series

• URL: http://arxiv.org/abs/2507.14206v1
• Date: Tue, 15 Jul 2025 02:54:24 GMT
• Title: A Comprehensive Benchmark for Electrocardiogram Time-Series
• Authors: Zhijiang Tang, Jiaxin Qi, Yuhua Zheng, Jianqiang Huang,
• Abstract summary: Electrocardiogram is crucial for assessing cardiac health and diagnosing various diseases.<n>ECG data is often incorporated into pre-training datasets for large-scale time-series model training.
• Score: 31.656774120734358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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