Medical Electrophysiological Data Automated Interpretation Deep Learning Model

The article is devoted to the development of a neural network model for automated analysis of medical data in the field of electrophysiology based on deep learning methods. The presented study is of high relevance due to the growing need for improvement of complex spatiotemporal signal processing. Such signals include electrocardiograms and electroencephalograms in clinical practice. Convolutional neural networks (CNNs), which extract spatial features, and temporal convolutional neural networks (TCNs), which specialize in identifying long-term time dependencies, were used as analysis tool. The integration of such architectures made it possible to identify and generalize complex patterns in signals and, subsequently, classify them with high accuracy. The paper presents a hybrid neural network model architecture developed for processing one-dimensional time series and provides a rationale for choosing key hyperparameters. To optimize the model, a strategy for adaptively changing the learning rate based on the value of the loss function on the validation dataset was implemented. An early training stopping mechanism was implemented to prevent redundant computations and to control overfitting in the event of no decrease in the error value on the validation data over a specified number of epochs. During training, model parameters were recorded after each epoch, including the epoch number, loss function value, and optimizer parameters. This allows for the reconstruction of training from any epoch and a detailed analysis of the model's behavior at various stages of training. The application of the developed analysis model enables more in-depth analysis of electrophysiological data, improving the objectivity of diagnostics. The methods presented in this paper can also be scaled to different biomedical data. The practical significance of the paper lies in the integration of the developed model into biomedical systems as a tool for supporting medical decisions, utilizing automated detection of pathologies and reducing the burden on doctors.