ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

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Language: 
English
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003098
EDN: 
RHZIPK

Using machine learning algorithms to determine the emotional maladjustment of a person by his rhythmogram

Autors: 
Stasenko Sergey Victorovich, Lobachevsky State University of Nizhny Novgorod
Shemagina Olga Vladimirovna, Institute of Applied Physics of the Russian Academy of Sciences
Eremin Evgeny Viktorovich, Lobachevsky State University of Nizhny Novgorod
Yakhno Vladimir Grigorevich, Institute of Applied Physics of the Russian Academy of Sciences
Parin Sergej Borisovich, Lobachevsky State University of Nizhny Novgorod
Polevaia Sofia Alexandrovna, Lobachevsky State University of Nizhny Novgorod
Abstract: 

The purpose of this study is to explore the feasibility of identifying emotional maladjustment using machine learning algorithms.

Methods. Electrocardiogram data were gathered using an event-telemetry approach, employing a software and hardware setup comprising a compact wireless ECG sensor (HxM; Zephyr Technology, USA) and a smartphone equipped with specialized software.For constructing the classifier, the following algorithms were employed: logistic regression, easy ensemble, and gradient boosting. The performance of these algorithms was assessed using the f1 metric.

Results. It is demonstrated that employing dynamic spectra of the original signals enhances the classification accuracy of the model compared to using the original rhythmograms.

Conclusion. A method is proposed for automatically determining the level of emotional maladaptation based on an individual’s cardiorhythmogram. Information from a portable heart sensor, worn by an individual, is transmitted via Bluetooth to a mobile device. Here, the level of emotional maladaptation is assessed through a pre-trained neural network algorithm. When considering a neural network algorithm, it is recommended to employ a classifier trained on spectrograms.

Key words: 
machine-learning algorithms
electcardiogram
emotional disadaptation
data analysis
Acknowledgments: 
The work in terms of collecting and data preprocessing was supported by the Russian Ministry of Science and Education project number 075-15-2021-634, the work in term of data analisis was supported by the frames of the Governmental Project of the Institute of Applied Physics RAS, project No. FFUF-2021-0014, the work in terms of developing the conceptual scheme of the experiment was supported by a grant from the Russian Science Foundation (project number 22-18-20075).
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Received: 
10.10.2023
Accepted: 
08.11.2023
Available online: 
15.03.2024
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