首个针对儿童心电图多标签心脏病分类的深度学习基准研究
A Benchmark Study of Deep Learning Methods for Multi-Label Pediatric Electrocardiogram-Based Cardiovascular Disease Classification
- 对比四种模型在9/12导联下的多标签心脏病分类性能
- 12导联下宏平均F1达94.67%，多数类别F1超85%
- 揭示罕见病识别难点，强调需更大样本和多中心验证
心血管疾病是儿童重大健康负担，早期筛查至关重要。心电图（ECG）作为无创且易获取的工具，非常适合该任务。本文首次基于新发布的ZZU-pECG数据集（3716条记录，19类心血管疾病），对四种代表性深度学习范式——ResNet-1D、BiLSTM、Transformer和Mamba 2——在9导联与12导联配置下的多标签儿童心脏病分类进行系统评估。所有模型表现强劲，汉明损失低至0.0069，多数情况下F1分数超过85%。其中，ResNet-1D在12导联子集上达到94.67%的宏平均F1，BiLSTM与Transformer也表现出色。逐类分析显示，在9导联配置中，肥厚性心肌病等罕见病因阳性样本有限而面临识别挑战。该基准建立了可复用的基线，并揭示了不同模型间的互补优势。研究进一步指出，未来需开展更大规模、多中心验证，进行年龄分层分析，并扩展疾病覆盖范围，以支持真实世界中的儿童心电图应用。
原文摘要 · Abstract (English)
Cardiovascular disease (CVD) is a major pediatric health burden, and early screening is of critical importance. Electrocardiography (ECG), as a noninvasive and accessible tool, is well suited for this purpose. This paper presents the first benchmark study of deep learning for multi-label pediatric CVD classification on the recently released ZZU-pECG dataset, comprising 3716 recordings with 19 CVD categories. We systematically evaluate four representative paradigms--ResNet-1D, BiLSTM, Transformer, and Mamba 2--under both 9-lead and 12-lead configurations. All models achieved strong results, with Hamming Loss as low as 0.0069 and F1-scores above 85% in most settings. ResNet-1D reached a macro-F1 of 94.67% on the 12-lead subset, while BiLSTM and Transformer also showed competitive performance. Per-class analysis indicated challenges for rare conditions such as hypertrophic cardiomyopathy in the 9-lead subset, reflecting the effect of limited positive samples. This benchmark establishes reusable baselines and highlights complementary strengths across paradigms. It further points to the need for larger-scale, multi-center validation, age-stratified analysis, and broader disease coverage to support real-world pediatric ECG applications.
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