Quantitative analysis of the effects of acoustic neurostimulation on the neuropsychology of healthy adults

To quantitatively analyze the effects of acoustic neurostimulation on the symptoms of depression, anxiety, stress, and sleep quality in healthy workers. Eleven physiological and psychological variables (V1–V11) representing stress levels, sleep quality, and cortisol levels were acquired from a recen...

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Bibliographic Details
Published in:Exploration of neuroprotective therapy 2024-08, Vol.4 (4), p.319-324
Main Authors: Radiance C. Bouldin, Julia R. Higdon, Jonghoon Kang
Format: Article
Language:English
Subjects:
acoustic neurostimulation
anxiety
depression
pca
sleep
stress
thermodynamic cycle
venn diagram
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Summary:To quantitatively analyze the effects of acoustic neurostimulation on the symptoms of depression, anxiety, stress, and sleep quality in healthy workers. Eleven physiological and psychological variables (V1–V11) representing stress levels, sleep quality, and cortisol levels were acquired from a recent article (https://doi.org/10.37349/ent.2023.00064) that analyzed the effects of brainwave entrainment (BWE) techniques—binaural beats (BB), isochronic tones (IT), or a combination of the two (BB + IT). Principal component analysis (PCA) was used to create principal components to analyze the contribution of each variable to the efficacy. A thermodynamic cycle and equations based on a Venn diagram were used to understand the differences in treatment effectiveness in individual and combined auditory stimulations. PCA reduced the dimensionality of variables from eleven to three. PC1 represented auditory treatment efficacy, while neither PC2 nor PC3 did. All eleven variables had a negative correlation to PC1, with stress (V3) showing the most negative correlation and salivary cortisol level (V11) showing the least. Treatments using BB were more effective than treatments with IT or BB + IT. PCA successfully aided in the analysis of auditory treatment efficacies. All examined variables, especially the stress scale (V3), had a negative correlation in treatment efficacy, aligning with the results of the original paper. Analysis using the thermodynamic cycle and Venn diagram based on PCA provided an explanation why a combined treatment (BB + IT) was less effective than BB alone in the collective consideration of all eleven variables. This study demonstrates that the thermodynamic cycle and Venn diagram in conjunction with PCA are useful analytical tools for the quantitative analysis of multi-factor systems.
ISSN:2769-6510
DOI:10.37349/ent.2024.00086