2 resultados para Classification and Regression Trees

em Digital Commons @ DU | University of Denver Research


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Author: Charity M. Walker Title: THE IMPACT OF SHYNESS ON LONELINESS, SOCIAL ANXIETY, AND SCHOOL LIKING IN LATE CHILDHOOD Advisor: Maria T. Riva, Ph.D. Degree Date: August 2011 ABSTRACT Shyness is associated with several emotional, social, and academic problems. While there are multiple difficulties that often accompany shyness, there appear to be some factors that can moderate negative effects of shyness. Research has demonstrated that certain parenting factors affect the adjustment of shy children in early childhood, but there is minimal research illuminating the effect of parenting factors in older age groups. The first purpose of this study was to examine relationships between shyness and loneliness, social anxiety, and school liking. The second purpose was to investigate whether the quality of the relationship between a parent and a 10- to 15-year-olds child influences the amount of loneliness or social anxiety a shy child experiences or how the child feels about school. Parent-child dyads served as participants and were recruited from public and private middle schools and church youth groups in Colorado and Indiana. Child participants completed several self-report surveys regarding their relationship with a parent, shyness, loneliness, social anxiety, and their attitude toward school. Parents completed a survey about their relationship with their child and responded to questions related to their perceptions of their child's shyness. Data was analyzed with a series of correlation and regression analyses. Greater degrees of self-reported shyness were found to be associated with higher levels of loneliness and social anxiety and less positive feelings about school. Due to a problem with multicollinearity during data analysis, this study was not able to explore the effect of the parent-child relationship quality on the associations between shyness and adjustment factors. Overall, these findings imply that shyness remains an important issue as children approach adolescence. Further research is needed to continue learning about the potential importance of parent-child interactions in reducing maladjustment for shy children during late childhood.

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Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.