Rafael Magdalena

BioLab: An Educational Tool for Signal Processing Training in Biomedical Engineering

This paper introduces and evaluates BioLab, a tool for teaching biosignal processing. BioLab has been used in the biomedical engineering module that is given in the second semester of the fifth year of the electronic engineering degree at the University of Valencia, Spain. This module and its correspondent curricular pathway are also reviewed. BioLab allows the results obtained with digital processing techniques to be shown interactively in the theory classes, and it also provides support in laboratory sessions. The graphic interface of BioLab simplifies its learning and use and provides access to processing and visualization functions by means of menus. The tool is based on Matlab since the students have had previous experience in this environment. BioLab also supports diverse formats of data files, which facilitate access to real records and their conversion to usable formats. The modular structure of BioLab allows it to be easily extended to other educational materials that are related to signal processing and to research applications. An evaluation of BioLab has revealed that students found it useful for understanding the general concepts of digital processing and biosignal processing in particular. The students also found BioLab very easy to learn and use

Feature selection using ROC curves on classification problems

Feature Selection (FS) is one of the key stages in classification problems. This paper proposes the use of the area under Receiver Operator Characteristic curves to measure the individual importance of every input as well as a method to discover the variables that yield a statistically significant improvement in the discrimination power of the classification model.

Survival prediction in patients undergoing ischemic cardiopathy

The ischemic cardiopathy is the main cause of death in developed countries. New improved drugs and therapies have appeared last years. However, the interventionist strategy and the most powerful drugs may have complications, and hence, it is very important to know the risk of death associated with patients during their stay in the hospital, or in the next six months. Thus, it is possible to tune the best treatment for each individual patient. In this framework, the use of artificial neural networks is proposed with a double objective: survival prediction and the extraction of the parameters with best predictive capabilities. A cohort of 691 patients treated in the Hospital Clínic, in Barcelona (Spain) during the period 2006–08 was used for this study. The obtained results show the good prediction capabilities of neural models when compared with classical models (logistic regression) and decision trees. Moreover, neural models reduced the number of relevant variables for the prediction from 134 to only 36.

Qualitative Analysis of Feed Management Practice on Goat Herds by Self Organizing Maps in Murcia Region of Spain

Abstract Fernández, C., Soria, E., Magdalena, R., Martín, J.D. and Mata, C. 2007. Qualitative analysis of feed management practice on goat herds by self organizing maps in Murcia region of Spain. J. Appl. Anim. Res., 32: 41–47. Self organizing maps (SOM) were used to analyze data from ninety four herds. Data were obtained from surveys and management practices were evaluated. The 18% of farms were dairy goat farms with milking machines, with a herd size of 100 to 200 goats and most of these bought compound feed. 12% has the same characteristics but farmers prepared their own diet. 16% were similar to previous prototypes, but farmers in addition to dairy goat production kept sheep as well. 23% of dairy goat farms practised hand milking. 14% of dairy goat farms had larger herd size (200–400) and fed goats with total mixed rations. Remaining 17% were sheep farms dedicated to lamb production for meat purposes. Such information can be used for better utilization of available resources in a particular area.

Adaptive algorithms robust to impulsive noise with low computational cost using Order Statistic

Abstract In this paper a family of adaptive algorithms robust to impulsive noise and with low computational cost are presented. Unlike other approaches, no cost functions or filtering of the gradient are considered in order to update the filter coefficients. Its initial basis is the basic LMS algorithm and its sign-error variant. The proposed algorithms can be considered as some sign-error variants of the LMS algorithm. The algorithms are successfully tested in terms of accuracy and convergence in a standard system identification simulation in which an impulsive noise is present. Simulations show that they improve the performance of LMS variants that are robust to impulsive noise.

P and R Wave Detection in Complete Congenital Atrioventricular Block

Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using noninvasive electrocardiographic surface leads. The proposed algorithm is divided into three stages. In the first stage, the R waves located by a QRS detector are used to generate the RR series and time references for the other stages of the algorithm. In the second stage, the ventricular activity (QT segment) is removed by using an adaptive filter that obtains an averaged pattern of the QT segment. In the third stage, a new P wave detector is applied to the residual signal obtained after QT cancellation in order to detect P wave locations and get the PP time series. Eight Holter records from patients with congenital type III AVB were used to verify the proposed algorithm. Although further improvements should be made to improve the algorithm’s performance, the results obtained show high average values of sensitivity (90.52%) and positive prediction (90.98%).

Design of a Neural Network Model as a Decision Making Aid in Renal Transplant

This paper presents the application of this new tool of data processing in the study of the problem that arises when a renal transplant is indicated for a paediatric patient. Its aim is the development and validation of a neural network based model which can predict the success of the transplant over the short, medium and long term, using pre-operative characteristics of the patient (recipient) and implant organ (donor). When compared to results of logistic regression, the results of the proposed model showed better performance. Once the model is obtained, it will be converted into a tool for predicting the efficiency of the transplant protocol in order to optimise the donor-recipient pair and maximize the success of the transplant. The first real use of this application will be as a decision aid tool for helping physicians and surgeons when preparing to perform a transplant.