从ECG信号到代表性形态热图用于生物识别
From ECG Signals to Representative-Morphology Heatmaps for Biometric Recognition
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中文总结 AI 辅助
本文提出代表性形态热图，将ECG信号转换为图像用于生物识别，通过多个模型和数据集验证，显著降低EER并提高识别率。
中文摘要 AI 辅助
心电图（ECG）包含支持生物识别的受试者特异性形态，然而基于图像的性能取决于波形的渲染方式。我们引入了代表性形态热图，这是一种从ECGXtractor改编而来的确定性ECG到图像的表示方法。在每个包含十个对齐心跳的块内，将最接近块均值的五个心跳平均为一个400乘L的矩阵，并渲染为常规轨迹或密集的心脏时间-导联热图。由于两种表示包含相同的生理样本，它们的比较可以隔离渲染效果。我们在PTB、ECG-ID和MIMIC-IV-ECG-DEMO上评估了验证和闭集识别。包括ZACH-ViT在内的五个紧凑模型从头开始训练，而六个ImageNet预训练的CNN和Transformer骨干网络评估了模型规模和视觉迁移。热图在全部15个紧凑模型-数据集比较中改善了FNMR操作点和两个识别排名，而EER在14个比较中有所改善。在匹配的实验中，EER平均降低了9.59个百分点，Rank-1平均提高了24.69个百分点。ConvNeXt-Tiny在PTB上达到2.43%的EER，在ECG-ID上达到5.79%，而DeiT-Base在MIMIC-DEMO上达到14.92%。ImageNet初始化明显有利于两个多导联数据集，但对ECG-ID效果不一，且性能不随模型尺寸单调增加。最佳热图系统在PTB和ECG-ID上接近最强的信号域EER，而DeiT-Base在MIMIC-DEMO上提供了最强的评估性能。导联通道消融进一步表明，有用的通道组合取决于队列和生物识别任务。总体而言，代表性形态热图为ECG验证和识别提供了一种有效的图像表示。
英文摘要
Electrocardiography (ECG) contains subject-specific morphology that supports biometric recognition, yet image-based performance depends on how the waveform is rendered. We introduce representative-morphology heatmaps, a deterministic ECG-to-image representation adapted from ECGXtractor. Within each block of ten aligned beats, the five beats closest to the block mean are averaged into a 400 by L matrix and rendered either as a conventional trace or as a dense cardiac-time-by-lead heatmap. Since both representations contain identical physiological samples, their comparison isolates the effect of rendering. We evaluate verification and closed-set identification on PTB, ECG-ID, and MIMIC-IV-ECG-DEMO. Five compact models, including ZACH-ViT, are trained from scratch, while six ImageNet-pretrained CNN and transformer backbones assess model scale and visual transfer. Heatmaps improve both FNMR operating points and both identification ranks in all 15 compact model-dataset comparisons, while EER improves in 14. Across the matched experiments, EER decreases by 9.59 percentage points and Rank-1 increases by 24.69 points on average. ConvNeXt-Tiny reaches 2.43% EER on PTB and 5.79% on ECG-ID, whereas DeiT-Base reaches 14.92% on MIMIC-DEMO. ImageNet initialization clearly benefits the two multilead datasets but has a mixed effect on ECG-ID, and performance does not increase monotonically with model size. The best heatmap systems approach the strongest signal-domain EER on PTB and ECG-ID, while DeiT-Base provides the strongest evaluated performance on MIMIC-DEMO. Lead-channel ablation further shows that useful channel combinations depend on the cohort and biometric task. Overall, representative-morphology heatmaps provide an effective image representation for ECG verification and identification.
发表机构
- University of the Bundeswehr Munich（慕尼黑联邦国防军大学）
- University of Amsterdam（阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。
