Application of sigmoidal functions based on atomic functions in a machine learning problem of the analysis of biomedical and biometric data

The paper proposes sigmoidal activation functions based on atomic functions. The properties of atomic functions are described, which allow them to satisfy the conditions for transfer functions in artificial neural networks. The applied problems of using the presented functions are considered on the example of problems of analysis of biophysical signals of cardiac activity. The results obtained using the constructed classifiers using various architectures of neural networks, including MLP, RNN, LSTM, GRU, CNN networks, are presented. The efficiency of using atomic functions in the constructed neural networks on the examples of the problem of automated diagnostics of pathologies based on seismocardiography data and biometric authentication by heart rate was determined using metrics of accuracy, recall, precision, sensitivity, specificity, and F-measure.