MARC 主機 00000nam  2200385   4500 
001    AAI3425085 
005    20110218114631.5 
008    110218s2010    ||||||||||||||||| ||eng d 
020    9781124255859 
035    (UMI)AAI3425085 
040    UMI|cUMI 
100 1  Gholami, Behnood 
245 10 Closed-loop control for cardiopulmonary management and 
       intensive care unit sedation using digital imaging 
300    182 p 
500    Source: Dissertation Abstracts International, Volume: 71-
       10, Section: B, page:  
500    Adviser: Wassim M. Haddad 
502    Thesis (Ph.D.)--Georgia Institute of Technology, 2010 
520    This dissertation introduces a new problem in the delivery
       of healthcare, which could result in lower cost and a 
       higher quality of medical care as compared to the current 
       healthcare practice. In particular, a framework is 
       developed for sedation and cardiopulmonary management for 
       patients in the intensive care unit. A method is 
       introduced to automatically detect pain and agitation in 
       nonverbal patients, specifically in sedated patients in 
       the intensive care unit, using their facial expressions. 
       Furthermore, deterministic as well as probabilistic expert
       systems are developed to suggest the appropriate drug dose
       based on patient sedation level 
520    Patients in the intensive care unit who require mechanical
       ventilation due to acute respiratory failure also 
       frequently require the administration of sedative agents. 
       The need for sedation arises both from patient anxiety due
       to the loss of personal control and the unfamiliar and 
       intrusive environment of the intensive care unit, and also
       due to pain or other variants of noxious stimuli. In this 
       dissertation, we develop a rule-based expert system for 
       cardiopulmonary management and intensive care unit 
       sedation. Furthermore, we use probability theory to 
       quantify uncertainty and to extend the proposed rule-based
       expert system to deal with more realistic situations 
520    Pain assessment in patients who are unable to verbally 
       communicate is a challenging problem. The fundamental 
       limitations in pain assessment stem from subjective 
       assessment criteria, rather than quantifiable, measurable 
       data.  The relevance vector machine (RVM) classification 
       technique is a Bayesian extension of the support vector 
       machine (SVM) algorithm which achieves comparable 
       performance to SVM while providing posterior probabilities
       for class memberships and a sparser model.  In this 
       dissertation, we use the RVM classification technique to 
       distinguish pain from non-pain as well as assess pain 
       intensity levels. We also correlate our results with the 
       pain intensity assessed by expert and non-expert human 
520    Next, we consider facial expression recognition using an 
       unsupervised learning framework. We show that different 
       facial expressions reside on distinct subspaces if the 
       manifold is unfolded. In particular, semi-definite 
       embedding is used to reduce the dimensionality and unfold 
       the manifold of facial images. Next, generalized principal
       component analysis is used to fit a series of subspaces to
       the data points and associate each data point to a 
       subspace. Data points that belong to the same subspace are
       shown to belong to the same facial expression 
520    In clinical intensive care unit practice sedative/
       analgesic agents are titrated to achieve a specific level 
       of sedation. The level of sedation is currently based on 
       clinical scoring systems. Examples include the motor 
       activity assessment scale (MAAS), the Richmond agitation-
       sedation scale (RASS), and the modified Ramsay sedation 
       scale (MRSS). In general, the goal of the clinician is to 
       find the drug dose that maintains the patient at a 
       sedation score corresponding to a moderately sedated 
       state. In this research, we use pharmacokinetic and 
       pharmacodynamic modeling to find an optimal drug dosing 
       control policy to drive the patient to a desired MRSS 
520    Atrial fibrillation, a cardiac arrhythmia characterized by
       unsynchronized electrical activity in the atrial chambers 
       of the heart, is a rapidly growing problem in modern 
       societies. One treatment, referred to as catheter ablation,
       targets specific parts of the left atrium for radio 
       frequency ablation using an intracardiac catheter.  As a 
       first step towards the general solution to the computer-
       assisted segmentation of the left atrial wall, we use 
       shape learning and shape-based image segmentation to 
       identify the endocardial wall of the left atrium in the 
       delayed-enhancement magnetic resonance images.  (Abstract 
       shortened by UMI.) 
590    School code: 0078 
650  4 Engineering, Aerospace 
650  4 Engineering, Biomedical 
690    0538 
690    0541 
710 2  Georgia Institute of Technology 
773 0  |tDissertation Abstracts International|g71-10B 
856 40 |u