Background: Meta-cognitive therapy and schema therapy are two important methods in treatment of mental disorders special in treatment of anxiety and depression. This study aimed to compare the effectiveness of meta-cognitive therapy and schema therapy on decrease symptoms of anxiety and depression in nursing and midwifery students.

Materials and Methods: This study was a quasi-experimental with a pre-test, post-test and 2 mounts follow-up design. The statistical population included all girl students of nursing and midwifery faculty that referred to counseling center of Islamic Azad University of Mashhad. Totally 60 students with anxiety and depression were selected through available sampling method and randomly assigned to three groups. The experimental groups educated 10 sessions of 70 minutes by meta-cognitive therapy and schema therapy methods. All groups completed the questionnaire of Cattell anxiety and Beck depression.The data were analyzed by using the SPSS-19 software and by multivariate analysis of covariance (MANCOVA) method.

Results: The findings showed in the post-test, schema therapy unlike meta-cognitive therapy significantly led to decrease symptoms of anxiety and depression. But in the follow-up state each two methods of meta-cognitive therapy and schema therapy significantly led to decrease symptoms of anxiety and depression and in the follow-up state, there wasn't any significant difference between two methods (p<0.05).

Conclusion: The results showed though the effect of schema therapy appears earlier than meta-cognitive therapy, but there is no significant difference between two treatments in long time Therefore, counselors and therapists can use meta-cognitive therapy and schema therapy for decreasing symptoms of anxiety and depression disorder.

Background: Many of key problems in students with attention deficit disorder are related to executive dysfunction that in this field neurofeedback training and computer games are effective. This study aimed to compare the effectiveness of neurofeedback training and computer games on continuous attention and planning ability in students with attention deficit disorder.

Materials and Methods: This study was a quasi-experimental with a pre-test and post-test design and with a control group. The statistical population was included all elementary students with attention deficit disorder that referred to counseling centers of Mashhad city in 2013. Totally, 45 students were selected through available sampling method and randomly assigned to three groups. The experimental groups were educated 12 sessions of 60 minutes by neurofeedback and computer games methods. To assess continuous attention and planning, the CPT and Tower of London computerized tests were used, respectively. Data were analyzed by using the SPSS-19 software and multivariate analysis of covariance (MONCOVA) methods.

Results: The findings showed that both methods of neurofeedback training and computer games significantly lead to increase continuous attention and planning in students with attention deficit disorder. Also, there wasn't any significant difference between two methods in continuous attention and planning (P<0/05).

Conclusion: The results showed that neurofeedback training and computer games methods were effective in improving continuous attention and planning for students with attention deficit disorder. Therefore, counselors and therapists can use mentioned methods in treatment of students with attention deficit disorder.

Background: Electrocardiogram signal (ECG) is a graphical representation of the heart activity. Processing and analysis of these morphological changes can result in visual diagnosing some cardiac diseases. However, various types of noises and disturbances in ECG influence the visual recognition and feature extraction from it. The aim of this research is to eliminate different noises from ECG and to enhance its quality.

Materials and Methods: In this study, an adaptive Kalman filter is developed by using Bayesian model. Considering simplification and Gaussian distribution for measurement noise, complicated mathematical equations were converted to simple relations and therefore implementation was simplified.

Results: In this paper, by designing an adaptive Kalman filter, the signal to noise ratio (SNR) has increased to 21.46dB. Adaptive Kalman filter based on Beyesian framework could model dynamic variations of ECG signal by estimating covariance matrix for measurement noise.

Conclusion: In despite of Kalman filters that use parametric functions to model ECG signal, the adaptive Kalman filter introduced in this paper uses real ECG records for modeling. Parametric functions which could model dynamic variations of ECG need a lot of analytical functions and this decreases the time of filtering process but the adaptive Kalman filter proposed in this research has a high speed and could be used in real time applications.