Convolutional neural network for classification of nerve activity based on action potential induced neurochemical Signatures

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 such applications, it is important to reliably classify pH signatures to control stimulation timing and possibly dosage. For example, the activity of the subdiaphragmatic vagus nerve (sVN) branch can be monitored by measuring extracellular neural pH. More importantly, gut hormone cholecystokinin (CCK)-specific activity on the sVN can be used for controllably activating sVN, in order to mimic the gut-brain neural response to food intake. 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 which enables, after training, a high accuracy classification of neurochemical signals using CNN

Resource efficient pre-processor for drift removal in neurochemical signals

A necessary requirement for chemometric platforms is pre-processing of the acquired chemical signals to remove baseline drift in the signal. The drift could originate from sensor characteristics or from background chemical activity in the surrounding environment. A recent emerging field is neurochemical monitoring to detect and quantify neural activity. In this paper, a resource efficient pre-processing system is presented to remove drift from the acquired neurochemical signal. The drift removal technique is based on baseline manipulation without requiring window based processing. The target application, for demonstration purposes, is the recording of vagal pH signals to enable closed-loop Vagus Nerve Stimulation (VNS). The final design is multiplier-free and results in an Application Specific Integrated Circuit (ASIC) that is 640 μm by 625 μm in area