An Abdominal Aortic Aneurysm (AAA) is a form of vascular disease causing focal enlargement of the abdominal aorta. It affects a large part of the population, and in case of rupture, has up to 90% mortality rate. Recent clinical recom- mendations suggest that people with small aneurysms should be examined 3-36 months depending on the size, to monitor morphological changes. While advances in biomechanics provide state-of-the-art spatial estimates of stress distributions of AAAs, there are still limitations in modeling its time evolution and uncertainty qualification. To date, there are a few biomechanical frameworks that utilize longitudinal medical images, which would aid physicians in detecting small aneurysms with high risk of rupture. In this study, we use longitudinal computer tomography (CT) scans of AAAs that are captured at different times to predict the spatio-temporal evolution of AAAs' shape in future time. We consider a surface of 3D AAA as a manifold embedded in a scalar field over the three dimensional space. The changes of the scalar field propagate into the changes in the surface. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden fields. First of all, we utilize the concept of the implicit surface field as a parameterization-free framework to describe a 3D shape. We then use Gaussian process regression to construct the field as an observation model from CT training image data. Furthermore, we propose a dynamic model to represent the evolution of the field. Finally, we derive the predicted surface from the predicted field. Our model is deployed on a real medical data set, which indicates its effectiveness. In addition, we discuss our prediction results with respect to ones from conventional analysis techniques.

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

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Abdominal Aortic Aneurysms (AAA) is a form of vascular disease causing focal enlargement of abdominal aorta. It affects a large part of population and has up to 90% mortality rate. Since risks from open surgery or endovascular repair outweighs the risk of AAA rupture, surgical treatments are not recommended with AAA less than 5.5cm in diameter. Recent clinical recommendations suggest that people with small aneurysms should be examined 3∼36 months depending on size to get information about morphological changes. While advances in biomechanics provide state-of-the-art spatial estimates of stress distributions of AAA, there are still limitations in modeling its time evolution. Thus, there is no biomechanical framework to utilize such information from a series of medical images that would aid physicians in detecting small aneurysms with high risk of rupture. For the present study, we use series of CT images of small AAAs taken at different times to model and predict the spatio-temporal evolution of AAA. This is achieved using sparse local Gaussian process regression.

In this work, we propose a temporal aggregation scheme for sentiments expressed in social networks. The proposed method discounts for the bias caused in aggregation due to classification errors while providing confidence intervals. A computationally efficient prediction and interpolation scheme of temporal progression is discussed that accounts for the heteroscedastic nature of noise. To this end, we use a heteroscedastic gaussian process model. To test the efficacy of our proposed method, we use tweets about Donald Trump obtained for a period of twelve hours. The results are generalized using six state of art classification schemes for predicting sentiments. Our method shows improvement in R 2 statistics with better coverage under proposed uncertainty for all the six classification schemes. Finally, the results of variational heteroscedastic gaussian process (VHGP) regression are discussed and the normalized mean square error with negative log-probabilty density of the prediction are reported. It is further shown that the volatility of opinion tracking in social network data streams is better captured with a heteroscedastic noise model.