ECGWmark：用于心电图信号的深度水印
ECGWmark: Deep Watermarking for Electrocardiogram Signals
AI总结：
针对ECG信号传输安全问题，提出无量化的ECGWmark深度水印框架，结合可微分生理损失提升鲁棒性，在MIT-BIH数据库实验中实现高比特提取准确率，同时保留诊断语义但存在类级效应。
摘要：
心电图（ECG）信号的安全传输仍是远程心脏病学中的重大挑战。传统数学水印可将患者元数据直接嵌入ECG信号，但对循环相移等时间失步极为脆弱。相比之下，现有针对音频等时间信号的深度水印方法通常依赖有损量化，会损害临床诊断关键的高频微形态细节。为填补这一空白，我们提出ECGWmark，一种用于医学时间序列的全卷积、无量化深度水印框架。ECGWmark在连续、全分辨率的潜在空间中运行，采用局部帧级机制提升对严重失步的鲁棒性。关键是，我们引入由临床分类器引导的可微分生理损失以保留诊断语义。在基准MIT-BIH数据库上的实验表明，ECGWmark在评估的循环时间偏移下保持信道比特提取准确率高于90%。此外，在严格的患者间诊断评估中，V类灵敏度从71.97%变为76.50%，Macro-F1从35.25%变为34.71%，S类灵敏度从8.89%变为1.39%；这些结果表明存在混合的类级效应。
英文摘要：
Secure transmission of electrocardiogram (ECG) signals remains a major challenge in telecardiology. While traditional mathematical watermarking can embed patient metadata directly into ECG signals, it is highly fragile against temporal desynchronization such as circular phase shifts. In contrast, existing deep watermarking approaches for temporal signals, such as audio, typically rely on lossy quantization, which compromises the high-frequency micro-morphological details critical for clinical diagnosis. To bridge this gap, we propose ECGWmark, a fully convolutional and quantization-free deep watermarking framework for medical time-series. ECGWmark operates in a continuous, full-resolution latent space and utilizes a localized frame-level mechanism to improve the robustness to severe desynchronization. Crucially, we introduce a differentiable Physiological Loss guided by a clinical classifier to preserve diagnostic semantics. Experiments on the benchmark MIT-BIH database show that ECGWmark maintains channel-bit extraction accuracy above 90% under the evaluated circular temporal shifts. Furthermore, under a rigorous inter-patient diagnostic evaluation, Class-V sensitivity changes from 71.97% to 76.50%, whereas Macro-F1 changes from 35.25% to 34.71% and Class-S sensitivity from 8.89% to 1.39%; these results indicate mixed class-level effects.
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