The Role of Big Five Personality Factors and Defense Mechanisms in Predicting Quality of Life in Sexually Dysfunctional Female Patients

Sexual dysfunction can lead to behavioral problems and reduction in a person's quality of life. In 50 % of patients with personality disorders, there is also sexual dysfunction. Psychoanalysis approach attributes the cause of sexual dysfunction to a kind of fundamental anxiety as well as the use of immature mechanisms in these patients. The purpose of this study was to investigate the role of big five personality traits and defensive mechanisms in predicting these patients' quality of life. Statistical sample of this research included 80 women attending sexual health and family clinics of Shahed University using accessible sampling during 2010 and 2011. These subjects were given the Neo Personality Inventory Traits, Defensive Mechanisms, and the World Health organization Quality of Life Questionnaires to answer. The findings showed that personality traits could predict the quality of life in woman with sexual dysfunction. Moreover, among those five personality traits, neuroticism (:./24 P=./04) and conscientiousness(:./31 P=./03) were able to predict the quality of life while predictability rate of both factors was 37% of variance on the whole (p=0/05). Based on regression analysis, there was a significant relationship between the quality of life and defensive mechanisms so that using more mature defensive mechanisms (:./37 P=./006) and immature defensive mechanisms (:-./31 P= ./02) could significantly predict quality of life (p=0/0001). Also, neurotic defensive mechanisms were not significant predictors of these women' quality of life. (;./04 P=./78)

Automatic sleep stage detection and classification: distinguishing between patients with periodic limb movements, Sleep Apnea Hypopnea Syndrome, and healthy controls using electrooculography (EOG) Signals

Background: To improve the diagnostic and clinical treatment of sleep disorders, the first important step is to identify or detect the sleep stages. Utilizing the conventional method-known as visual sleep stage scoring-is tedious and time-consuming. Therefore, there is a significant need to create or develop a new automatic sleep stage detection system to assist the sleep physician in evaluating the sleep stages of patients or non-patient subjects. The first aim of this study is to develop an algorithm for automatic sleep stage detection based on Electrooculography (EOG) signals. The second aim is to utilize sleep quality parameters to classify and screen Periodic Limb Movements of Sleep (PLMS) patients and Sleep Apnea Hypopnea Syndrome (SAHS) patients, as distinct from healthy control subjects. Methods: 10 patients with Periodic Limb Movements of Sleep (PLMS), 10 patients with Sleep Apnoea Hypopnea Syndrome (SAHS), and 10 healthy control subjects were utilised in this study. Several features were extracted from EOG signals such as cross-correlation, energy entropy, Shannon entropy and maximal amplitude value. K-Nearest Neighbour was used for the classification of sleep stages. Several polysomnographical (PSG) features were measured for screening and classification of the sleep disorders, such as the percentage of the sleep stages over the total time of sleep, the duration of the sleep stages, Sleep Latency (SL), and sleep efficacy. A decision tree analysis was utilised for identifying the three groups of subjects. Results: The overall accuracy, sensitivity and specificity of automatic sleep stage detection were 80.5%, 81.3% and 88.8%, respectively. The Cohen’s Kappa was 0.73. The performance of the classified sleep disorders showed an overall accuracy of 90%. The sensitivity and specificity were 90% and 95%. The Cohen’s Kappa was 0.85. Conclusion: One advantage of the automatic sleep stage detection method based on Electrooculography (EOG) signals is that it can be utilized with portable sleep stage recording instead of using a multichannel signal. Classification of sleep disorders based on the automatic system is an improvement, in that it can make the screening or diagnostic processes much faster and easier than with other methods

The Effect of Hypericum Perforarum on Anxiety and Depressant Activity in Wistar Rats Exposed to Phenol

Introduction: For a Long time, anxiety has been an important issue in psychology and different drugs have been applied to treat it. Various studies have demonstrated that the plant Hypericum perforatum has an antidepressant effect. With the industrialization of human societies, pollutants like phenol can be entered in the life cycle that have adverse effects on body organs. Therefore, this study aimed to investigate the antianxiety and antidepressant effects of Hypericum Perforarum extract in rats that were exposed to phenol. Methods: In this study, 54 Wistar rats were used in terms of a 3×3 factorial design with 3 levels of Hypericum perforatum extract (0, 250 and 500 mg/kg.bw) and 3 levels of phenol (0, 100 and 200 mg/kg.bw). Rats received the extract and phenol every other day via gavage method for periods of 15 and 30 days, respectively. Thirty min after each gavage, a behavior test was performed by using the open field and elevated plus-maze. Recuperative effects of Hypericum perforatum were assessed within short (first 15 days) and long (second 15 days) periods. Results: The statistic findings indicated that there were no significant differences between behavior tests with respect to the treatments (P> 0.05). Conclusion: The study results proposed that the used levels of Hypericum Perforarum extract did not show any significant effects on reduction of anxious and depressant behaviors in phenol exposed Wistar rats

Application of Random Forest Classifier for Automatic Sleep Spindle Detection

Sleep spindle detection using supervised learning methods such as Artificial Neural Networks and Support Vector Machines had been researched in the past. Supervised learning methods such as the above are prone to overfitting problems. In this research paper, we explore the detection of sleep spindles using the Random Forest classifier which is known to over fit data to a much lower extent when compared to other supervised classifiers. The classifier was developed using data from 3 subjects and it was tested on data from 12 subjects from the MASS database. A sensitivity of 71.2% and a specificity of 96.73% was achieved using the random forest classifier

Automatic Detection of Sleep Arousal Events from Polysomnographic Biosignals

Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consuming and subjective. Our objective of this study was to develop an algorithm for automatic detection of sleep arousals without distinguishing between the types of arousal and sleep disorder groups. The processed and analysed data multiple overnight Polysomnography (PSG) recordings, consisting of 9 human subjects (6 male, 3 female), with age range of 34-69 and different conditions (4 patients with obstructive sleep apnoeas, 4 healthy and 1 patient with periodic limb movement disorder). PSG biosignals were processed to extract necessary features. Knearest neighbours (KNN) was used as the classifier and performance of algorithm were evaluated by Leave-One-Out Cross-Validation. The average sensitivity, specificity and accuracy of algorithm was 79%, 95.5% and 93%, respectively. These results demonstrate that our algorithm can automatically detect arousals with high accuracy. Furthermore, the algorithm is capable to be upgraded for classification of various types of arousals based upon their origin and characteristics

Sleep Onset Detection with Multiple EEG Alpha-Band Features: Comparison Between Healthy, Insomniac and Schizophrenic Patients

In the past several studies have evaluated the human sleep onset (wake to sleep transition) using the electroencephalographic (EEG) measurements. This paper has evaluated the detection accuracy of sleep stages for multiple features based on the EEG alpha activity, during SO in healthy, insomniac and schizophrenic patients. The features include topographic features such as Directed Transfer Function, Full frequency DTF, Welch Coherence, Minimum Variance Distortionless Response Coherence and Partial Directed Coherence. Sleep stages Wake, NREM (Non-rapid Eye Movement) stages 1 and 2 were classified using Artificial Neural Networks (ANN) classifier and evaluated using classification accuracy. The results suggest that using topographic set of features yield an agreement of 81.3 % with the whole database classification of human expert