Classification of uterine EMG signals using supervised classification method

Abstract

Aim: The main purpose of this article is to detect any risk of preterm deliveries at an early gestation period using uterine electromyography signals. Detecting such uterine signals can yield a promising approach to determine and take actions to prevent this potential risk. Methods: The best position for the detection of different uterine signals is the median vertical axis of the abdomen. These signals differ from each other by their frequency content. Initially, simulation is done for the real detected EMG signals: preterm deliveries (PD) EMGs and deliveries at term (DT) EMGs. This is performed by applying autoregressive model (AR) of specific order to estimate AR coefficients of these real EMG signals. Finally, after calculation of the AR parameters of the two types of deliveries, we generate two types of simulated uterine contractions by using White Gaussian Noise (WGN). Frequency parameter extraction and classification are first applied on simulated signals to test the limits and performance of the used methods. The last remaining step is the classification of the contractions using supervised classification method. Results: Results show that uterine contractions may be classified using the Artificial Neural Networks (ANNs). The Simple Perceptron ANN is applied on the signals for their supervised classification into independent groups: preterm deliveries (PD) and deliveries at term (TD) according to their frequency content.