IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i10p5913-d1152823.html
   My bibliography  Save this article

Stress Response Analysis via Dynamic Entropy in EEG: Caregivers in View

Author

Listed:
  • Ricardo Zavala-Yoé

    (Tecnológico de Monterrey, Calzada del Puente, 222. Col. Ejidos de Huipulco, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Hafiz M. N. Iqbal

    (Tecnológico de Monterrey, Eugenio Garza Sada 2501, Monterrey 64849, Mexico
    These authors contributed equally to this work.)

  • Roberto Parra-Saldívar

    (Tecnológico de Monterrey, Eugenio Garza Sada 2501, Monterrey 64849, Mexico
    These authors contributed equally to this work.)

  • Ricardo A. Ramírez-Mendoza

    (Tecnológico de Monterrey, Eugenio Garza Sada 2501, Monterrey 64849, Mexico
    These authors contributed equally to this work.)

Abstract

According to the World Health Organization (WHO), stress can be defined as any type of alteration that causes physical, emotional, or psychological tension. A very important concept that is sometimes confused with stress is anxiety. The difference between stress and anxiety is that stress usually has an existing cause. Once that activator has passed, stress typically eases. In this respect, according to the American Psychiatric Association, anxiety is a normal response to stress and can even be advantageous in some circumstances. By contrast, anxiety disorders differ from temporary feelings of anxiousness or nervousness with more intense feelings of fear or anxiety. The Diagnostic and Statistical Manual (DSM-5) explicitly describes anxiety as exorbitant concern and fearful expectations, occurring on most days for at least 6 months, about a series of events. Stress can be measured by some standardized questionnaires; however, these resources are characterized by some major disadvantages, the main one being the time consumed to interpret them; i.e., qualitative information must be transformed to quantitative data. Conversely, a physiological recourse has the advantage that it provides quantitative spatiotemporal information directly from brain areas and it processes data faster than qualitative supplies. A typical option for this is an electroencephalographic record (EEG). We propose, as a novelty, the application of time series (TS) entropies developed by us to inspect collections of EEGs obtained during stress situations. We investigated this database related to 23 persons, with 1920 samples (15 s) captured in 14 channels for 12 stressful events. Our parameters reflected that out of 12 events, event 2 (Family/financial instability/maltreatment) and 10 (Fear of disease and missing an important event) created more tension than the others. In addition, the most active lobes reflected by the EEG channels were frontal and temporal. The former is in charge of performing higher functions, self-control, self monitoring, and the latter is in charge of auditory processing, but also emotional handling. Thus, events E2 and E10 triggering frontal and temporal channels revealed the actual state of participants under stressful situations. The coefficient of variation revealed that E7 (Fear of getting cheated/losing someone) and E11 (Fear of suffering a serious illness) were the events with more changes among participants. In the same sense, AF4, FC5, and F7 (mainly frontal lobe channels) were the most irregular on average for all participants. In summary, by means of dynamic entropy analysis, the goal is to process the EEG dataset in order to elucidate which event and brain regions are key for all participants. The latter will allow us to easily determine which was the most stressful and on which brain zone. This study can be applied to other caregivers datasets. All this is a novelty.

Suggested Citation

  • Ricardo Zavala-Yoé & Hafiz M. N. Iqbal & Roberto Parra-Saldívar & Ricardo A. Ramírez-Mendoza, 2023. "Stress Response Analysis via Dynamic Entropy in EEG: Caregivers in View," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:10:p:5913-:d:1152823
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/10/5913/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/20/10/5913/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:10:p:5913-:d:1152823. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.