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The problem of recognition of emotions in humans is still open from a few aspects. In our study, we used a deep learning method named stacked autoencoder to classify regions of four emotional states in valencearousal plane (high valence-low arousal, high valence-high arousal, low valence-low arousal, and low valence-high arousal). We used a number of physiological signals, including electroencephalogram (EEG), electromyogram (EMG), and other peripheral signals from the DEAP database and extracted spectral and time features from these signals. Also, nonlinear features were extracted from EEG. Then these features were imported to multiple stacked autoencoders in a parallel form (PSAE) to primarily classify four emotional regions in a valence-arousal plane. The final decision about classification was performed using the majority voting method. The average accuracy for classifying four emotional regions from the valence arousal plane, i.e., low arousal and low valence (LALV), low arousal and high valence (LAHV), high arousal and high valence (HAHV), and high arousal and low valence (HALV), reached 93.6%. This result shows that our proposed method demonstrates certain advantages in solving the classification problem; probably, it can also be used for other classification problems. [ABSTRACT FROM AUTHOR]

Background: Mutations in breast cancer type 1 and type 2 (BRCA 1/2) genes have been identified in populations expressing a strong family history of breast and ovarian cancers and account for 5-10 % of cases of breast cancer. The options for reconstruction in this cohort can be challenging because of the young age at which many patients present the sizeable defect and the psychosocial effect it has on their lives. A multidisciplinary one-stop clinic (MDOSC) is fundamental to their management Methods: Our study included 91 patients, from July 2007 to July 2012, who underwent RRM with immediate autologous or implant-based reconstruction within our department. Women were invited to participate in the study post surgery and completed a validated Breast-Q questionnaire detailing their experience in a number of domains including body image and perception, sexuality, and cosmetic outcome. Patients were also invited to attend an individual interview for further analysis of patient experience and expectations. Results: Using the questionnaire responses and private interview content, a detailed analysis of BRCA patient experience was achieved. The main concerns identified included expectations regarding the outcome and the dissatisfaction rates, less than 20 %, correlated with postoperative complication rates. Patients emphasized the importance of careful preoperative consultation with a multidisciplinary team, and >90 % cited a desire to recommend RRM with immediate reconstruction to other patients that met similar criteria. Conclusions: RRM with immediate reconstruction is a good option for this unique group of patients; BRCA positive and overall patient satisfaction is high. Our study also highlights the significance of a multidisciplinary team working toward improving patient experience and outcome as well as managing patient expectations prior to surgery. Level of Evidence: Level III, risk/prognostic study. [ABSTRACT FROM AUTHOR]