BeatGraph：家庭环境下婴儿心电表征的自监督心跳图
BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment
- University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有ECG基础模型忽略心脏结构、难以适应婴儿高心率的问题，提出BeatGraph，以心跳为基本单元构建图表示，通过自监督预训练和微调，在多项婴儿任务上显著提升性能，并发布首个公共婴儿ECG语料库。
AI中文摘要：
心电图（ECG）基础模型通常将信号分割成固定长度的片段，这些片段忽略了心脏结构，因此一个片段可能分割一次心跳，且每个片段中的心跳次数会随心率变化。这对婴儿影响最大，因为婴儿心率更高，且其心电图不同于这些模型所基于的成人临床记录的12导联数据。因此，用于婴儿的ECG模型应直接处理心跳，而不是从任意片段中恢复心跳。我们提出BeatGraph，将心跳作为表征单位，将每个30秒窗口建模为心跳图。共享的心跳编码器从波形和心跳间间隔中嵌入每次心跳，带有位置编码的Transformer按时间顺序排列心跳，残差图注意力层将每次心跳与其他所有心跳关联，随后注意力池化生成窗口嵌入。我们在新的无标签婴儿记录语料库上通过预测掩蔽心跳嵌入来预训练BeatGraph，然后针对每个任务进行微调。一个主干支持睡眠-觉醒检测、婴儿状态分类、活动来源识别（婴儿或看护者发起的运动）以及情感识别，在每项任务上相比最强基线将宏F1提高了0.076至0.158。它还能跨年龄组迁移，在ZZU-pECG儿科基准（0至14岁）上达到0.892的AUROC，与最佳已发表的自监督ECG模型相差0.001以内，并且尽管仅使用婴儿数据进行预训练，在成人PTB-XL基准的线性评估中与该模型表现相当。最后，据我们所知，我们发布了首个在家庭、教室和实验室环境中收集的带有状态和情感标签的公共婴儿ECG语料库。该语料库包含来自143名3至11个月婴儿的3,408小时单通道ECG，并提供无标签预训练数据、基准任务和受试者级别的划分。
英文摘要：
Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of beats in each patch shifts with heart rate. This matters most for infants, whose heart rates are higher and whose ECG differs from the adult, clinic-recorded 12-lead data these models are built on. A model for infant ECG should therefore reason about heartbeats directly rather than recover them from arbitrary patches. We propose BeatGraph, which makes the heartbeat its unit of representation, modeling each 30-second window as a graph of beats. A shared beat encoder embeds each heartbeat from its waveform and inter-beat intervals, a Transformer with positional encoding orders the beats in time, and residual graph attention layers relate every beat to every other before attention pooling yields a window embedding. We pretrain BeatGraph on our new corpus of unlabeled infant recordings by predicting masked-beat embeddings, then fine-tune it for each task. One backbone supports sleep-wake detection, infant-state classification, activity-source identification (infant- or caregiver-initiated movement), and affect recognition, improving macro-F1 over the strongest baseline on each task by 0.076 to 0.158. It also transfers across age groups, reaching 0.892 AUROC on the ZZU-pECG pediatric benchmark (ages 0 to 14), within 0.001 of the best published self-supervised ECG model, and matching that model under linear evaluation on the adult PTB-XL benchmark despite infant-only pretraining. Finally, to our knowledge, we release the first public infant ECG corpus collected in homes, classrooms, and laboratory settings with state and affect labels. It contains 3,408 hours of single-channel ECG from 143 infants aged 3 to 11 months, with unlabeled pretraining data, benchmark tasks, and subject-level splits.
