Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records

Abstract

Abstract. Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate.

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care

“…Other studies have found medium frequency to be more helpful in determining whether delivery was going to be term or preterm [6], [15]. These along with amplitude based features, root mean squares, and sample entropy will be considered and extracted from the raw EHG signals.…”