Fetal weight estimation for prediction of fetal macrosomia: does additional clinical and demographic data using pattern recognition algorithm improve detection?

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.

Classification of finger activation for use in a robotic prosthesis arm.

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.