Data-Driven Classifiers for Early Meal Detection Using ECG

Abstract

This study investigates the potential of the electrocardiogram (ECG) to perform early meal detection, which is critical for developing a fully-functional automatic artificial pancreas. The study was conducted in a group of healthy subjects with different ages and genders. Two classifiers were trained: one based on neural networks (NNs) and working on features extracted from the signals; one based on convolutional NNs (CNNs) and working directly on raw data. During the test phase, both classifiers correctly detected all the meals, with the CNN outperforming the NN in terms of misdetected meals (MMs) and detection time (DT). Reliable meal onset detection with short detection time has significant practical implications: it reduces the risk of postprandial hyperglycemia and hypoglycemia, it reduces the mental burden of meal documentation for patients and related stress.

Data-Driven Classifiers for Early Meal Detection Using ECG