Hervaldo Sampaio Carvalho

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

BackgroundTwo-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.MethodsThis work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.ResultsThe proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The methods intra- and inter-operator variabilities are also evaluated.ConclusionsThe proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.

Energy efficient simulator for patient monitoring in Body Sensor Networks

The medical tracking of patients carried out from a distance by means of wireless technology, incorporating various sensors simultaneously connected to the body of an individual, can promote health in suitable time. This article proposes a simulator for the monitoring of human health. This prototype simulates the workings of a BSN (Body Sensor Network) functioning as a sensor (electrophysiological signal) that is to be monitored, which utilizes a strategy based on the concept of data fusion. This tool incorporates a policy of economizing energy which has as its main objective increasing the operating time of the system.

A Framework for Automated Evidence Gathering with Mobile Systems Using Bayesian Networks

This article presents a framework that allows the automated gathering of medical evidence, using a methodology based on Bayesian networks (BN). For such purpose, system software and a programming methodology were developed. The proposed methodology is generic and can be applied to different contexts (application domains) were evidence gathering is executed in mobile devices.

1138 Measurement of beat-to-beat variability of stroke volume

Introduction Stroke volume variability (SVV) provides information about the activity of the autonomic nervous system, connecting heart rate variability (HRV) to blood pressure and venous return variabilities [1]. There is currently no noninvasive gold-standard for measuring stroke volume (SV). Recent MR methods can measure cardiac output by integrating flow volume through several cardiac cycles [2,3]. We propose a method that is capable of measuring changes in SV on a beat-to-beat basis.

The Diagnosis Support System for Ischemic Cardiopathy: A Case Study in the Context of IACVIRTUAL Project

The Artificial Intelligence Applied in the Modeling and Implementation of a Virtual Medical Office Project (IACVIRTUAL) proposes an intelligent system to simulate a Web-based Medical Office conceived to support (i) patients who are interested in following their medical reports, (ii) professionals interested in decisions support systems for diagnosis and treatment, and (iii) students interested in learning by following available medical cases. IACVIRTUAL has several modules and among them are the Decision Support Module (DSM) and the Educational Module (EM). The Intelligent Tutor System (ITS), a sub-module of EM, is based on the MATHEMA architecture, which was conceived to provide a space for promoting cooperative interactions between an apprentice and a society of artificial tutor agents, having a human expert’s society as a backup assistance for such interactions. DSM supports medical diagnosis and subsidies EM with problem resolution using Case-Based Reasoning (CBR). CBR is an Artificial Intelligence technique that solves a new problem using adaptations of solutions of already known similar problems. The CBR model proposed for IACVIRTUAL is based on the Cross-industry Standard Process for Data Mining (CRISP-DM) and follows the 4R (Recuperation, Reutilization, Revision and Retention) cycle. Using (i) this model as fundament, (ii) a base with 1052 clinical records referring to the disease Ischemic Cardiopathy, (iii) the support of a medical expert, (iv) methods of Data Mining, and (v) the IACVIRTUAL environment; a system was developed to support medical diagnosis in the field of Cardiology. This paper intends to describe this system as well to present a case study where it was used in the context of IACVIRTUAL project.

A New Model for Programming Software in Body Sensor Networks

A body sensor network (BSN) must be designed to work autonomously. On the other hand, BSNs need mechanisms that allow changes in their behavior in order to become a clinically useful tool. The purpose of this paper is to present a new programming model that will be useful for programming BSN sensor nodes. This model is based on an intelligent intermediate-level compiler. The main purpose of the proposed compiler is to increase the efficiency in system use, and to increase the lifetime of the application, considering its requirements, hardware possibilities and specialist knowledge. With this model, it is possible to maintain the autonomous operation capability of the BSN and still offer tools that allow users with little grasp on programming techniques to program these systems.