Pierluigi Salvo Rossi

Abstract 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).

Abstract One of the challenges for the diabetic patients is to regulate the amount of glucose in the blood. Early and reliable meal detection represents one relevant issue to develop more effective treatments. This paper presents a comparison of different classifiers for early meal detection using abdominal sounds. The data presented in the paper is obtained from two different equipment and the classifiers are trained and tested on twelve recordings. The results show that neural networks and convolutional neural networks provide better average detection time (2.