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

Natalia Konnova; Mikhail Basarab

doi:10.1117/12.2612001

11 November 2021 Application of sigmoidal functions based on atomic functions in a machine learning problem of the analysis of biomedical and biometric data

Natalia Konnova, Mikhail Basarab

Author Affiliations +

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760S (2021) https://doi.org/10.1117/12.2612001
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

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.

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Natalia Konnova and Mikhail Basarab "Application of sigmoidal functions based on atomic functions in a machine learning problem of the analysis of biomedical and biometric data", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760S (11 November 2021); https://doi.org/10.1117/12.2612001

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