Using machine learning algorithms to determine the emotional disadaptation of a person by his rhythmogram
This work addresses emotional health assessment for individuals, but it appears incremental as it applies existing machine learning methods to a new type of physiological data.
The study tackled the problem of detecting emotional disadaptation in individuals by analyzing their rhythmogram, specifically using ECG signals, and demonstrated that this approach can effectively register such emotional states.
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.