Heart to Heart, March 2012

Heart to Heart is a publication on new heart disease and stroke information and other related topics by the Department of Public Health.

Heart to Heart, April 2012

Heart to Heart is a publication on new heart disease and stroke information and other related topics by the Department of Public Health.

Effect of the output of the system in signal detection

We analyze the consequences that the choice of the output of the system has in the efficiency of signal detection. It is shown that the output signal and the signal-to-noise ratio (SNR), used to characterize the phenomenon of stochastic resonance, strongly depend on the form of the output. In particular, the SNR may be enhanced for an adequate output.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Audit report on the Heart of Iowa Regional Transit Agency, Des Moines, for the year ended June 30, 2011

Audit report on the Heart of Iowa Regional Transit Agency, Des Moines, for the year ended June 30, 2011

Effects of dexamethasone on the metabolic responses to mental stress in humans.

The haemodynamic effects of the sympathetic nervous system (SNS) activations elicited by hypoglycaemia, acute alcohol administration, or insulin can be prevented by a pretreatment with dexamethasone in humans. This suggests a possible role of central corticotropin releasing hormone (GRIT) release. Mental stress activates the SNS, and decreases systemic vascular resistances though a beta-adrenergic-mediated vasodilation thought to involve vascular nitric oxide release. It also increases insulin-mediated glucose disposal, an effect presumably related to vasodilation. In order to evaluate whether activation of SNS by mental stress is glucocorticoid-sensitive, we monitored the haemodynamic and metabolic effects of mental stress during hyperinsulinaemia in healthy humans with and without a 2-day treatment with 8 mg day(-1) dexamethasone. Mental stress decreased systemic vascular resistances by 21.9% and increased insulin-mediated glucose disposal by 2 8.4% without dexamethasone pretreatment. After 2 days of dexamethasone treatment, whole body insulin-mediated glucose disposal was decreased by 40.8%. The haemodynainic effects of mental stress were however, not affected. Mental stress acutely increased insulin-mediated glucose disposal by 28.0%. This indicates that mental stress elicits a stimulation of SNS through dexamethasone-insensitive pathway, distinct of those activated by insulin, alcohol, or hyperglycaemia.

Heart to Heart, May 2012

Heart to Heart is a publication on new heart disease and stroke information and other related topics by the Department of Public Health.

Heart to Heart, June 2012

Heart to Heart is a publication on new heart disease and stroke information and other related topics by the Department of Public Health.

Heart to Heart, July 2012

Heart to Heart is a publication on new heart disease and stroke information and other related topics by the Department of Public Health.

Heart to Heart, August 2012

Heart to Heart is a publication on new heart disease and stroke information and other related topics by the Department of Public Health.

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.

Subclass problem-dependent design for error-correcting output codes

A common way to model multiclass classification problems is by means of Error-Correcting Output Codes (ECOCs). Given a multiclass problem, the ECOC technique designs a code word for each class, where each position of the code identifies the membership of the class for a given binary problem. A classification decision is obtained by assigning the label of the class with the closest code. One of the main requirements of the ECOC design is that the base classifier is capable of splitting each subgroup of classes from each binary problem. However, we cannot guarantee that a linear classifier model convex regions. Furthermore, nonlinear classifiers also fail to manage some type of surfaces. In this paper, we present a novel strategy to model multiclass classification problems using subclass information in the ECOC framework. Complex problems are solved by splitting the original set of classes into subclasses and embedding the binary problems in a problem-dependent ECOC design. Experimental results show that the proposed splitting procedure yields a better performance when the class overlap or the distribution of the training objects conceal the decision boundaries for the base classifier. The results are even more significant when one has a sufficiently large training size.