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

TL;DR

This paper demonstrates that machine learning algorithms can analyze rhythmogram data, specifically ECG signals, to identify emotional disadaptation in individuals, offering a new approach for emotional state assessment.

Contribution

The study introduces a method combining machine learning with cardiorhythmography to detect emotional disadaptation from ECG signals.

Findings

ECG signals can be used to register emotional disadaptation

Machine learning algorithms effectively analyze rhythmogram data

Proposed method provides a new tool for emotional state assessment

Abstract

In this study we applyed machine-learning algorithms to determine the emotional disadaptation of a person by his rhythmogram. We used the method of determining a subject level of emotional disadaptation and recording of cardiorhythmography. We show that electrocardiogram (ECG) signals can be used for the registration of the emotional disadaptation of a person.