用半监督知识蒸馏提升可穿戴设备心电诊断能力
Approaching Low-Cost Cardiac Intelligence with Semi-Supervised Knowledge Distillation
- 设计区域感知蒸馏模块，模拟心脏病专家关注关键心电波段
- 在5个数据集上使低成本系统性能接近高价系统，宏平均F1提升4.27%~7.10%
- 适合想用可穿戴设备实现高精度日常心脏监测的开发者
将先进的心脏人工智能应用于日常心脏监测面临医疗数据量大和计算资源需求高的挑战。低成本心脏智能（LCCI）利用可穿戴设备的单导联心电图（ECG）数据提供替代方案，但其诊断性能显著低于高成本心脏智能（HCCI）。为此，我们提出LiteHeart，一种半监督知识蒸馏框架。LiteHeart引入区域感知蒸馏模块，模拟心脏病专家对诊断相关心电区域的关注；并设计跨层互信息模块，对齐LCCI与HCCI系统的决策过程。采用半监督训练策略，进一步提升模型在有限标注下的鲁棒性。在涵盖超过38种心血管疾病的5个数据集上评估，LiteHeart显著缩小了LCCI与HCCI之间的性能差距，宏平均F1得分优于现有方法4.27%至7.10%。结果表明，LiteHeart大幅提升了低成本心脏智能系统的诊断能力，为基于可穿戴技术实现可扩展、低成本且精准的日常心脏健康管理铺平道路。
原文摘要 · Abstract (English)
Deploying advanced cardiac artificial intelligence for daily cardiac monitoring is hindered by its reliance on extensive medical data and high computational resources. Low-cost cardiac intelligence (LCCI) offers a promising alternative by using wearable device data, such as 1-lead electrocardiogram (ECG), but it suffers from a significant diagnostic performance gap compared to high-cost cardiac intelligence (HCCI). To bridge this gap, we propose LiteHeart, a semi-supervised knowledge distillation framework. LiteHeart introduces a region-aware distillation module to mimic how cardiologists focus on diagnostically relevant ECG regions and a cross-layer mutual information module to align the decision processes of LCCI and HCCI systems. Using a semi-supervised training strategy, LiteHeart further improves model robustness under limited supervision. Evaluated on five datasets covering over 38 cardiovascular diseases, LiteHeart substantially reduces the performance gap between LCCI and HCCI, outperforming existing methods by 4.27% to 7.10% in macro F1 score. These results demonstrate that LiteHeart significantly enhances the diagnostic capabilities of low-cost cardiac intelligence systems, paving the way for scalable, affordable, and accurate daily cardiac healthcare using wearable technologies.
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