Severity Estimation of Depression Using Convolutional Neural Network

Authors

  • Attila Zoltán Jenei ORCID
    Affiliation

    Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar tudósok körútja 2, Hungary

  • Gábor Kiss
    Affiliation

    Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar tudósok körútja 2, Hungary

https://doi.org/10.3311/PPee.15958

Abstract

In the present study, we attempt to estimate the severity of depression using a Convolutional Neural Network (CNN). The method is special because an auto- and cross-correlation structure has been crafted rather than using an actual image for the input of the network. The importance to investigate the possibility of this research is that depression has become one of the leading mental disorders in the world. With its appearance, it can significantly reduce an individual's quality of life even at an early stage, and in severe cases, it may threaten with suicide. It is therefore important that the disorder be recognized as early as possible. Furthermore, it is also important to determine the disorder severity of the individual, so that a treatment order can be established. During the examination, speech acoustic features were obtained from recordings. Among the features, MFCC coefficients and formant frequencies were used based on preliminary studies. From its subsets, correlation structure was created. We applied this quadratic structure to the input of a convolutional network. Two models were crafted: single and double input versions. Altogether, the lowest RMSE value (10.797) was achieved using the two features, which has a moderate strength correlation of 0.61 (between estimated and original).

Keywords:

BDI estimation, convolutional neural network, depression, MFCC

Published Online

2021-06-29

How to Cite

Jenei, A. Z., Kiss, G. “Severity Estimation of Depression Using Convolutional Neural Network”, Periodica Polytechnica Electrical Engineering and Computer Science, 65(3), pp. 227–234, 2021. https://doi.org/10.3311/PPee.15958

Issue

Section

Articles