Pattern Recognition of Neurotransmitters: Complexity Reduction for Serotonin and Dopamine.

In this work, we simultaneously detected and predicted the concentration levels of serotonin (SE) and dopamine (DA) neurotransmitters (NTs) for in vitro mixtures, with measurements obtained using conventional glassy carbon electrodes (CGCEs) and differential pulse voltammetry (DPV). The NTs were estimated by deconvolving the multiplexed signals of both NTs using Principal Component Analysis with Gaussian Process Regression (PCA-GPR) and Partial Least Squares with Gaussian Process Regression (PLS-GPR), both with exponential-isotropic kernels. The average testing accuracies of estimation using PCA-GPR for DA alone, SE alone and their mixture (DA-SE) were 87.6%, 88.1%, and 96.7%, respectively. Using PLS-GPR, the testing accuracies of estimation for DA alone, SE alone, and their mixture (DA-SE) were 87.3%, 83.8%, and 95.1%, respectively. Furthermore, we explored methods of reducing the procedural complexity in estimating the NTs by finding reduced subsets of features for accurately detecting and predicting their concentrations. The reduced subsets of features found in the oxidation potential windows of the NTs improved the testing accuracy of the estimation of DA-SE to 97.4%. We thus believe that reducing complexity has the potential to increase the detection and prediction accuracies of NT measurements for practical clinical uses such as deep brain stimulation.