The hypothesis was that the 1D-CNN would perform best due to its unique ability to extract spatially correlated features from the information-rich voltage responses. The DNN was expected to be more prone to overfitting and computationally intensive due to the larger number of parameters involved in its architecture. However, it facilitates a direct comparison to the 1D-CNN, highlighting the effectiveness of its convolutional layers. Finally, the SVM was hypothesized to achieve high accuracy if the voltage responses in the feature space were clean and well-separated, but struggle if noisy and overlapping.