Why the study?
Early meal detection is critical for developing a fully-functional automatic artificial pancreas, prompting investigation into the potential of electrocardiogram signals.
Does an ECG-based CNN classifier improve early meal detection compared to a feature-based NN classifier in healthy subjects?
Does an ECG-based CNN classifier improve early meal detection compared to a feature-based NN classifier in healthy subjects?
ECG-based convolutional neural networks can reliably detect early meal onset, which may aid in developing fully-functional automatic artificial pancreas systems.
ECG-based meal detection remains exploratory in healthy volunteers; leaves open validation for closed-loop artificial pancreas in diabetes.
This letter investigates the potential of the electrocardiogram 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 and 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 and detection time (DT). Reliable meal onset detection with short DT has significant practical implications: It reduces the risk of postprandial hyperglycemia and hypoglycemia, and it reduces the mental burden of meal documentation for patients and related stress.
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Cheema et al. (2023) studied this question.
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