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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Published in | Applied sciences Vol. 15; no. 2; p. 934 |
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Main Authors | , , |
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Language | English |
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ISSN | 2076-3417 2076-3417 |
DOI | 10.3390/app15020934 |
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Abstract | 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 (ssp), distance (d), and size (ssz), 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. |
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AbstractList | 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]), distance (d), and size (s[sub.sz]), 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. 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 (ssp), distance (d), and size (ssz), 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. |
Audience | Academic |
Author | Chang, Sungbok Park, Hyunjun Suh, Jungho |
Author_xml | – sequence: 1 givenname: Jungho orcidid: 0009-0004-1530-3749 surname: Suh fullname: Suh, Jungho – sequence: 2 givenname: Sungbok orcidid: 0009-0004-7051-5269 surname: Chang fullname: Chang, Sungbok – sequence: 3 givenname: Hyunjun surname: Park fullname: Park, Hyunjun |
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Cites_doi | 10.1186/s12888-022-04320-4 10.7812/TPP/13-098 10.1109/ACCESS.2020.3028182 10.3389/fpsyg.2017.01409 10.5014/ajot.2020.041442 10.3389/fpsyt.2015.00045 10.1016/0006-3223(90)90591-O 10.1097/WNP.0000000000000316 10.1111/pme.12303 10.3390/s21134360 10.17547/kjsr.2017.25.4.272 10.3390/scipharm84040724 10.1016/j.janxdis.2018.08.003 10.1037/0021-843X.100.4.535 10.3389/fpsyg.2017.01578 10.1016/j.bandc.2004.08.005 10.1002/jclp.1126 10.3390/e22111239 10.1097/NMD.0b013e318142cf73 10.1016/S1388-2457(00)00527-7 10.1016/S0006-3223(96)00260-0 10.1891/1933-3196.2.4.247 10.1080/17538947.2018.1447032 10.1002/cne.920180503 10.1016/S0079-7421(08)60452-1 10.1176/appi.ajp.158.3.405 10.1109/TIM.2019.2947126 10.3390/polym12081812 |
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SubjectTerms | artificial neural network Brain research Deep learning depression Depression, Mental EEG Electroencephalography EMDR Evidence-based medicine Evidence-based practice Eye movements Health aspects Information processing Memory Mental depression Mental health Neural networks Neurophysiology Post traumatic stress disorder Regression analysis Variables video stimuli parameters |
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Title | Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy |
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