Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy
Eye Movement Desensitization and Reprocessing (EMDR) was recognized by the World Health Organization in 2013 as an evidence-based therapy for post-traumatic stress disorder (PTSD) and found to be effective for depression. Since then, EMDR has evolved into a personalized treatment focusing on stabili...
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MDPI AG
2025-01-01
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| author | Jungho Suh Sungbok Chang Hyunjun Park |
| author_facet | Jungho Suh Sungbok Chang Hyunjun Park |
| author_sort | Jungho Suh |
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| description | Eye Movement Desensitization and Reprocessing (EMDR) was recognized by the World Health Organization in 2013 as an evidence-based therapy for post-traumatic stress disorder (PTSD) and found to be effective for depression. Since then, EMDR has evolved into a personalized treatment focusing on stabilizing the physiological and psychological processes to alleviate symptoms of depression and stress. However, optimized parameters for video stimuli, such as speed (s<sub>sp</sub>), distance (d), and size (s<sub>sz</sub>), are not yet well defined in EMDR protocols. This study addresses this gap by employing an artificial neural network (ANN) methodology based on Francine Shapiro’s Adaptive Information Processing (AIP) model. The ANN was used to determine ideal values for video stimuli parameters, developing an integrated model to enhance EMDR outcomes. Of the 2860 ANN-modeled combinations, stimulus settings of 1.8 Hz speed, 70-pixel size, and 1440-pixel distance achieved the highest Predicted Effectiveness Score (PES) of 98.7%. An EMDR field test with electroencephalography (EEG) was conducted to assess the optimized video stimuli’s efficacy. Further, 16 participants, selected from a sample of 56 meeting CES-D depression criteria, were evaluated, and the top 50 PES values were selected for further analysis. EEG results indicated a 12.31% increase in effectiveness, showing a reduction in right frontal lobe beta waves. These findings highlight the technical advancements and therapeutic potential of the proposed ANN-optimized EMDR stimuli, demonstrating statistically significant improvements over traditional methods. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-f9f799ae90964d69b7f344685bffdf6b2025-01-24T13:21:23ZengMDPI AGApplied Sciences2076-34172025-01-0115293410.3390/app15020934Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment EfficacyJungho Suh0Sungbok Chang1Hyunjun Park2Department of Media Communication, College of Social Science, Gachon University, Seongnam 13120, Republic of KoreaDepartment of Digital Media Design, College of Arts, Cheongju University, Cheongju 28496, Republic of KoreaDivision of Artificial Intelligence Engineering, National Korea Maritime & Ocean University, Busan 49112, Republic of KoreaEye Movement Desensitization and Reprocessing (EMDR) was recognized by the World Health Organization in 2013 as an evidence-based therapy for post-traumatic stress disorder (PTSD) and found to be effective for depression. Since then, EMDR has evolved into a personalized treatment focusing on stabilizing the physiological and psychological processes to alleviate symptoms of depression and stress. However, optimized parameters for video stimuli, such as speed (s<sub>sp</sub>), distance (d), and size (s<sub>sz</sub>), are not yet well defined in EMDR protocols. This study addresses this gap by employing an artificial neural network (ANN) methodology based on Francine Shapiro’s Adaptive Information Processing (AIP) model. The ANN was used to determine ideal values for video stimuli parameters, developing an integrated model to enhance EMDR outcomes. Of the 2860 ANN-modeled combinations, stimulus settings of 1.8 Hz speed, 70-pixel size, and 1440-pixel distance achieved the highest Predicted Effectiveness Score (PES) of 98.7%. An EMDR field test with electroencephalography (EEG) was conducted to assess the optimized video stimuli’s efficacy. Further, 16 participants, selected from a sample of 56 meeting CES-D depression criteria, were evaluated, and the top 50 PES values were selected for further analysis. EEG results indicated a 12.31% increase in effectiveness, showing a reduction in right frontal lobe beta waves. These findings highlight the technical advancements and therapeutic potential of the proposed ANN-optimized EMDR stimuli, demonstrating statistically significant improvements over traditional methods.https://www.mdpi.com/2076-3417/15/2/934EMDRdepressionartificial neural networkvideo stimuli parametersEEG |
| spellingShingle | Jungho Suh Sungbok Chang Hyunjun Park Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy Applied Sciences EMDR depression artificial neural network video stimuli parameters EEG |
| title | Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy |
| title_full | Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy |
| title_fullStr | Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy |
| title_full_unstemmed | Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy |
| title_short | Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy |
| title_sort | optimization of video stimuli parameters in emdr therapy using artificial neural networks for enhanced treatment efficacy |
| topic | EMDR depression artificial neural network video stimuli parameters EEG |
| url | https://www.mdpi.com/2076-3417/15/2/934 |
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