The possibility of application a convolutional neural network to assess the augmentation of electrical images is proposed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%. This paper outlines measurements of several devices and a process for simulating large amounts of annotated data based on those measurements. A convolutional neural net (CNN) algorithm for classifying signals is described. Performance of the CNN is compared to that of dynamic time warping (DTW) and correlation algorithms, with respect to varying training data set size and noise level. The CNN has the best performance for almost all cases considered.