用可穿戴心电图实现进食行为实时监测，还能回溯判断依据。
Cardiac Evidence Backtracking for Eating Behavior Monitoring using Collocative Electrocardiogram Imagining
- 将心电信号转为伪图像，适配二维深度模型
- 通过周期注意力机制引导模型提取可解释证据
- 支持证据回溯，结果与医学研究一致
进食行为监测长期面临无创连续传感器缺失和自动检测方法不可靠的挑战。本文首次利用可穿戴24小时心电图（ECG）进行传感，并设计深度学习框架实现即时、可解释的进食行为检测。核心方法包括：1）将一维心电信号构建为伪图像张量，提升二维图像模型的适用性；2）提出周期性注意力调节机制，以类人逻辑分析心电数据，引导模型在推理中收集可理解的证据；3）通过类激活映射（CAM）解码与决策树/森林生成，实现证据的可追溯性。该框架在最大规模进食行为心电数据集上验证，性能优于传统模型，且回溯证据与既往医学研究高度一致，证明其具备心脏证据挖掘能力。
原文摘要 · Abstract (English)
Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and the reliable methods for automatic behavior detection. In this paper, we present a pilot study using the wearable 24-hour ECG for sensing and tailoring the sophisticated deep learning for ad-hoc and interpretable detection. This is accomplished using a collocative learning framework in which 1) we construct collocative tensors as pseudo-images from 1D ECG signals to improve the feasibility of 2D image-based deep models; 2) we formulate the cardiac logic of analyzing the ECG data in a comparative way as periodic attention regulators so as to guide the deep inference to collect evidence in a human comprehensible manner; and 3) we improve the interpretability of the framework by enabling the backtracking of evidence with a set of methods designed for Class Activation Mapping (CAM) decoding and decision tree/forest generation. The effectiveness of the proposed framework has been validated on the largest ECG dataset of eating behavior with superior performance over conventional models, and its capacity of cardiac evidence mining has also been verified through the consistency of the evidence it backtracked and that of the previous medical studies.
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