Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

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Abstract:Patients with motor neuron disease and most terminal patients cannot use their hands or arms, and so they need another person for their all needs. However, the mental functions and memories of such patients are generally sound, and they can control their eyes. Using an eye-gaze tracking technique, we have realized a real-time system for such patients. The system controls a motorized electrical hospital bed (EHB) by eye gaze with 4 degrees of freedom, using a low-cost webcam. Contactless systems that require calibration cannot be used for EHB control. The system developed in this work does not require any calibration process and it is contactless. These properties are the most innovative part of the proposed approach. To begin, the system detects the eye region and computes the iris centers. It then tracks the centers and moves a mouse pointer on a screen with the eye gaze. The specific movements of the mouse pointer are evaluated as position changing requests and the completed movements of the mouse pointer change the EHB position electrically. The communication between the computer and the EHB is provided by a relay control card driven by Arduino Mega. The system works under day/artificial lighting conditions successfully with or without eyeglasses.The system was tested with 30 volunteers on the EHB safely and was completed with 90% success (the exceptions being people with slanted eyes).

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We studied the promoter methylation status and expression levels of P16 and CDH1 genes in breast cancer and their adjacent normal tissues with normal control breast tissues, to correlate with their histopathological parameters. Hundred twenty four samples (tumor and adjacent nonmalignant tissues) from 62 breast cancer patients and 4 normal control breast tissues were included in the study. We used methylation specific PCR to evaluate methylation status and quantitative RT-PCR to measure the gene expression levels. Methylation incidence of P16 gene and CDH1 gene in tumor tissues were 24.2 % and 33.9 %, respectively. CDH1 and P16 gene were not methylated in normal control tissues. CDH1 underexpression is found to be significant in correlation with advanced stage, histologic type, high tumor grade and lymph node involvement. P16 expression is found not to be significantly related with any histopathological parameters. But 60% of cases which overexpresses P16 were estrogen negative, and 40% of them were histologic grade 3. Both P16 and CDH1 had different expression levels in tumor tissues compared to the adjacent normal tissues and in adjacent normal tissues compared to the normal non-tumor tissues. Key words: Breast cancer, P16, CDH1, methylationhomophilic cell-cell adhesion in epithelial tissues. CDH1 (E Cadherin), a Ca++ dependent transmembrane glycoprotein functioning in cell to cell adhesion placed in 16q22.1, is one of the cardinal regulators of morphogenesis [7]. Decreased levels of CDH1 expression related with the advanced stage and poorly differentiated cancers [8].We aimed to find out any possible relationship and concordance between promoter methylation status and expression levels of CDH1 and P16 genes, two critical genes in the carcinogenesis, with the histopathological parameters in sporadic breast cancer and adjacent normal breast tissue. Materials and methodsBreast cancer primary tissues. Breast tumors and adjacent nonmalignant tissues from resection margin are obtained from the Dokuz Eylül Breast Tumor Biobank (DEBTB) under permission of local clinical and laboratory research ethical council for analysis of patient samples. All the breast tissue samples were collected from patients, who had neither chemotherapy nor radiotherapy before operation. Tissue samples were constituted of tumor cells at average of 60% of the whole specimens Changes in the status of DNA methylation represent one of the most common molecular alterations in human neoplasia [1], including breast cancer [2]. Methylation in breast cancer has been related to clinical and pathologic characteristics evident at presentation and clinical outcomes. A higher prevalence of HIN-1 and RAR β2 methylation was found in the lymph nodes, bone, brain, and lung metastases than the primary tumor [3]. Widschwendter and colleagues [4] reported that the methylation of certain genes was associated with hormone receptor status, in addition to the response to treatment with tamoxifen.An uncontrolled cell division requires further progression through G1 p...