Volume 23, Issue 105 (10-2013)                   J Mazandaran Univ Med Sci 2013, 23(105): 2-7 | Back to browse issues page

XML Persian Abstract Print

Abstract:   (6940 Views)
Background and purpose: EEG is used as a diagnostic tool in the diagnosis and prognosis of disease spread. Among the psychiatric illnesses that can utilize EEG to measure response to treatment, schizophrenia can be noted. Many investigations on the application of data EEG as a predictor of treatment response in patients with schizophrenia refractory to clozapine has been done in order to resolve contradictions and the limitations of previous studies. We studied the response to treatment with clozapine, with the PANSS greater number of samples making them prior studies have examined. Material & methods: In a cross-sectional study, 70 patients with schizophrenia resistant to treatment with candidates receiving clozapine were selected from those EEG and PANSS baseline was patient during twelve weeks of treatment with clozapine were again patients PANSS was used and EEG raw patients with a good response to treatment and disease with poor response to treatment were compared with each other. After data collection, descriptive statistics software SPSS17 square test was used for the analysis of EEG data from a linear regression to examine the relationship between the indicators of response to clozapine treatment. Results: The results showed that there is coherence of EEG and the treatment response to clozapine significant correlation was (P= 0.00). The results showed that there is presence of asymmetry in EEG and the treatment response to clozapine significant correlation was (P= 0.8). Conclusion: The EEG abnormalities include coherence of EEG in predicting response to treatment in patients with treatment-resistant schizophrenia to clozapine which is effective.
Full-Text [PDF 141 kb]   (1977 Downloads)    
Type of Study: Research(Original) | Subject: Sport Medicine

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.