Cardiac Evidence Backtracking for Eating Behavior Monitoring using Collocative Electrocardiogram Imagining

TL;DR

This paper introduces a novel deep learning framework using collocative ECG analysis for non-invasive, interpretable eating behavior monitoring, validated on a large dataset with promising results.

Contribution

It proposes a new collocative learning approach that converts ECG signals into pseudo-images and incorporates cardiac logic for explainable eating behavior detection.

Findings

Superior performance over traditional models

Effective cardiac evidence backtracking

Validated on the largest ECG eating behavior dataset

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.