Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

An improved EEG pattern classification system based on dimensionality reduction and classifier fusion

University of Technology, Sydney. Faculty of Engineering and Information Technology.Analysis of brain electrical activities (Electroencephalography, EEG) presents a rich source of information that helps in the advancement of affordable and effective biomedical applications such as psychotropic drug research, sleep studies, seizure detection and brain computer interface (BCI). Interpretation and understanding of EEG signal will provide clinicians and physicians with useful information for disease diagnosis and monitoring biological activities. It will also help in creating a new way of communication through brain waves. This thesis aims to investigate new algorithms for improving pattern recognition systems in two main EEG-based applications. The first application represents a simple Brain Computer Interface (BCI) based on imagined motor tasks, whilst the second one represents an automatic sleep scoring system in intensive care unit. BCI system in general aims to create a lion-muscular link between brain and external devices, thus providing a new control scheme that can most benefit the extremely immobilised persons. This link is created by utilizing pattern recognition approach to interpret EEG into device commands. The commands can then be used to control wheelchairs, computers or any other equipment. The second application relates to creating an automatic scoring system through interpreting certain properties of several biomedical signals. Traditionally, sleep specialists record and analyse brain signal using electroencephalogram (EEG), muscle tone (EMG), eye movement (EOG), and other biomedical signals to detect five sleep stages: Rapid Eye Movement (REM), stage 1,... to stage 4. Acquired signals are then scored based on 30 seconds intervals that require manually inspecting one segment at a time for certain properties to interpret sleep stages. The process is time consuming and demands competence. It is thought that an automatic scoring system mimicking sleep expert rules will speed up the process and reduce the cost. Practicality of any EEG-based system depends upon accuracy and speed. The more accurate and faster classification systems are, the better will be the chance to integrate them in wider range of applications. Thus, the performance of the previous systems is further enhanced using improved feature selection, projection and classification algorithms. As processing EEG signals requires dealing with multi-dimensional data, there is a need to minimize the dimensionality in order to achieve acceptable performance with less computational cost. The first possible candidate for dimensionality reduction is employed using channel feature selection approach. Four novel feature selection methods are developed utilizing genetic algorithms, ant colony, particle swarm and differential evolution optimization. The methods provide fast and accurate implementation in selecting the most informative features/channels that best represent mental tasks. Thus, computational burden of the classifier is kept as light as possible by removing irrelevant and highly redundant features. As an alternative to dimensionality reduction approach, a novel feature projection method is also introduced. The method maps the original feature set into a small informative subset of features that can best discriminate between the different class. Unlike most existing methods based on discriminant analysis, the proposed method considers fuzzy nature of input measurements in discovering the local manifold structure. It is able to find a projection that can maximize the margin between data points from different classes at each local area while considering the fuzzy nature. In classification phase, a number of improvements to traditional nearest neighbour classifier (kNN) are introduced. The improvements address kNN weighting scheme limitations. The traditional kNN does not take into account class distribution, importance of each feature, contribution of each neighbour, and the number of instances for each class. The proposed kNN variants are based on improved distance measure and weight optimization using differential evolution. Differential evolution optimizer is utilized to enhance kNN performance through optimizing the metric weights of features, neighbours and classes. Additionally, a Fuzzy kNN variant has also been developed to favour classification of certain classes. This variant may find use in medical examination. An alternative classifier fusion method is introduced that aims to create a set of diverse neural network ensemble. The diversity is enhanced by altering the target output of each network to create a certain amount of bias towards each class. This enables the construction of a set of neural network classifiers that complement each other

Documentation in health care

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Phase Shifts of the Circadian Locomotor Rhythm Induced by Pigment-Dispersing Factor in the Cricket Gryllus bimaculatus

Pigment-dispersing factors (PDFs) are octadeca-peptides widely distributed in insect optic lobes and brain. In this study, we have purified PDF and determined its amino acid sequence in the cricket Gryllus bimaculatus. Its primary structure was NSEIINSLLGLPKVLNDA-NH2, homologous to other PDH family members so far reported. When injected into the optic lobe of experimentally blinded adult male crickets, Gryllus-PDF induced phase shifts in their activity rhythms in a phase dependent and dose dependent manner. The resulted phase response curve (PRC) showed delays during the late subjective night to early subjective day and advances during the mid subjective day to mid subjective night. The PRC was different in shape from those for light, serotonin and temperature. These results suggest that PDF plays a role in phase regulation of the circadian clock through a separate pathway from those of other known phase regulating agents

Methods for the Analysis of Pretest-Posttest Binary Outcomes from Cluster Randomization Trials

In this thesis we investigate methods for assessing the intervention effect in completely randomized, cluster randomization trials where participants are measured prior to random assignment and again after completion of the intervention, i.e. a pretest-posttest design. Attention is further limited to binary outcomes. We compare the performance of six test statistics used to test the intervention effect. Test statistics are obtained from cluster-specific and population-averaged extensions of logistic regression. A simulation study is used to estimate type I error and power for the test statistics. In addition, we examine the effect on power of correctly assuming a common pretest-posttest association. Cluster-specific models yielded satisfactory 5% type I error rates while a longitudinal approach yielded the lowest power. Assumptions about the pretest-posttest association had little effect on power. Data from a school-based randomized trial are used to illustrate results

CO2 and O2 variability in the partially ice-covered Arctic Ocean

Limited carbon cycle research has been conducted so far in the Arctic Ocean (AO) compared to many other open-ocean and coastal environments, with relatively few studies of the inorganic carbon cycle and air-sea gas exchange. Understanding these processes in depth and understanding the physical, chemical, and biological processes that control carbon dioxide (CO2) and dissolved oxygen (DO) variability in the AO are crucial to predicting the future of the carbon cycle in the region and its impact on greenhouse gases and marine ecosystem processes, such as ocean acidification. To study the AO carbon cycle, in situ time-series data have been collected from the Canada Basin of the AO during late summer to autumn of 2012. Partial pressure of CO2 (pCO2), DO concentration, temperature, salinity, and chlorophyll-a fluorescence (Chl-a) were measured at 6-10 m depth under little ice and multi-year ice on two drifting platforms. The pCO2 levels were always below atmospheric saturation, whereas the seawater was almost always slightly supersaturated with respect to DO. Although the two time-series data were on an average only 222 km apart they had 10 ± 10% and 63 ± 16% ice cover and differed significantly in contributions from gas exchange and net community production (NCP). Modeled variability of CO2 and DO suggest that gas exchange, NCP and horizontal gradients are the main sources of the CO2 and DO variability in the partially ice-covered AO. Horizontal gradients dominated the more densely ice-covered region, with no significant NCP in the surface. These results suggest that the signature imparted on CO2 and DO in open water is widely disbursed under-ice and that biological production under multi-year ice is negligible due to lack of light and nutrients

Creation and Implementation of Process FMEA with Focus on Risk Reduction for Packaging Process

A Twin Cities electronic device manufacturer, with its increasing customers in medical device industry, decided to get certified for ISO 13485:2003 and ISO 14971. As a result of this the company is implementing risk based approach to different process to fulfill the requirement of ISO 13485 and ISO 14971.This capstone project focuses on studying the packaging process and conducting risk analysis on this process. The project includes creating process flow chart, and calculating and managing risk using FMEA for packaging process. FMEA which stands for Failure mode and effect analysis is a proactive tool developed to identify, evaluate and prevent product and/or process failures. The project studies the packaging process and helps identifying different failure modes (FM) for each of the process input, determining effect of each of the FM, identifying causes for the FM, analyzing severity, quantifying occurrences and detectability to each of the FM, calculating risk priority number, assessing risk and mitigating risk according to Risk Management Plan for the company. This includes conducting risk-benefit analysis as well