Cardiac Evidence Backtracking for Eating Behavior Monitoring using Collocative Electrocardiogram Imagining
Journal:
arXiv
Published Date:
Feb 20, 2025
Abstract
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.
