Preterm Birth Prediction by Classification of Spectral Features of Electrohysterography Signals using 1D Convolutional Neural Network: Preliminary Results

The assessment of uterine contraction provides valuable information about the progress of labour. Deliveries occurring before the estimated date introduce undesirable consequences for the fetus and mother. Electrohysterography (EHG), a noninvasive monitoring method, is the most preferred and studied method to diagnose preterm delivery and minimize its unpleasant consequences. This paper proposes a novel feature extraction method for non-invasive diagnosis of preterm delivery using EHG signals. The acquired EHG signals are sliced into frames for spectral analysis. Next, the centroid frequencies of the frames are computed to establish a feature vector. Using a 1D Convolutional Neural Network (CNN), the effectiveness of the feature vector is compared with existing methods in the literature. The experimental results confirm that the proposed method shows superior performance and can be used to predict term-preterm birth more accurately.