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

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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.

About the authors

Sergey Victorovich Stasenko

Lobachevsky State University of Nizhny Novgorod

ORCID iD: 0000-0002-3032-5469
Scopus Author ID: 55327776400
ResearcherId: J-4825-2013
603950 Nizhny Novgorod, Gagarin Avenue, 23

Olga Vladimirovna Shemagina

Institute of Applied Physics of the Russian Academy of Sciences

ul. Ul'yanova, 46, Nizhny Novgorod , 603950, Russia

Evgeny Viktorovich Eremin

Lobachevsky State University of Nizhny Novgorod

ORCID iD: 0000-0001-5707-6063
Scopus Author ID: 57196752333
603950 Nizhny Novgorod, Gagarin Avenue, 23

Vladimir Grigorevich Yakhno

Institute of Applied Physics of the Russian Academy of Sciences

ORCID iD: 0000-0002-4689-472X
Scopus Author ID: 35554909600
ResearcherId: L-1813-2017
ul. Ul'yanova, 46, Nizhny Novgorod , 603950, Russia

Sergej Borisovich Parin

Lobachevsky State University of Nizhny Novgorod

ORCID iD: 0000-0001-5721-8762
Scopus Author ID: 6602724059
603950 Nizhny Novgorod, Gagarin Avenue, 23

Sofia Alexandrovna Polevaia

Lobachevsky State University of Nizhny Novgorod

ORCID iD: 0000-0002-3896-787X
Scopus Author ID: 6504648647
ResearcherId: C-7512-2012
603950 Nizhny Novgorod, Gagarin Avenue, 23

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