Neural activity results in chemical changes in the extracellular environment such as variation in pH or potassium/sodium ion concentration. Higher signal to noise ratio make neurochemical signals an interesting biomarker for closed loop neuromodulation systems. For example, the activity of the subdiaphragmatic vagus nerve(sVN) branch can be monitored through measure pH extracellularly. In this paper, we present a convolutional neural network (CNN) based classification system to identify CCK-specific neurochemical changes on the sVN, from non-linear background activity. Here we present a novel feature engineering approach base on a CNN, which enables, after training, a high accuracy classification of neurochemical signals.