Convolutional Neural Networks (CNNs) have traditionally been used for image analysis and computer vision and are known for their ability to detect complex patterns in data. This report studies an application of CNNs within bioelectronic medicine, namely predicting blood glucose levels using nerve signals. Nerve signals and blood glucose levels were measured on a mouse before and after administration of glucose injections. The nerve signals were measured by placing 16 voltage-measuring electrodes on the vagus nerve of the mouse. The obtained nerve signal data was segmented into time intervals of 5 ms and aligned with the corresponding glucose measurements. Two LeNet-5 based CNN architectures, one 1-dimensional and one 2-dimensional, were implemented and trained on the data. Evaluation of the models’ performance was based on the mean squared error, the mean absolute error, and the R2-score of a simple moving average over the dataset. Both models had promising performance with an R2-score of above 0.92, suggesting a strong correlation between nerve signals and blood glucose levels. The difference in performance between the 1-dimensional and 2-dimensional model was insignificant. These results highlight the potential of using CNNs in bioelectronic medicine for prediction of physiological parameters from nerve signal data.

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