ABSTRACT
OBJECTIVE: The aim of this study was to test whether pattern recognition classifiers with multiple clinical and sonographic variables could improve ultrasound prediction of fetal macrosomia over prediction which relies on the commonly used formulas for the sonographic estimation of fetal weight. METHODS: THE SVM ALGORITHM WAS USED FOR BINARY CLASSIFICATION BETWEEN TWO CATEGORIES OF WEIGHT ESTIMATION: >4000gr and <4000gr. Clinical and sononographic input variables of 100 pregnancies suspected of having LGA fetuses were tested. RESULTS: Thirteen out of 38 features were selected as contributing variables that distinguish birth weights of below 4000gr and of 4000gr and above. Considering 4000gr. as a cutoff weight the pattern recognition algorithm predicted macrosomia with a sensitivity of 81%, specificity of 73%, positive predictive value of 81% and negative predictive value of 73%. The comparative figures according to the combined criteria based on two commonly used formulas generated from regression analysis were 88.1%, 34%, 65.8%, 66.7%. CONCLUSIONS: The SVM algorithm provides a comparable prediction of LGA fetuses as other commonly used formulas generated from regression analysis. The better specificity and better positive predictive value suggest potential value for this method and further accumulation of data may improve the reliability of this approach.
ABSTRACT
Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are selected by a genetic algorithm as inputs for a simple classifier. This method results in a probability of error of less than 2%.
