Building A Deep Learning Model Using Grammian Angular Field Encoding Of Time-Series Cardiotocography Images

Abstract

Fetal well being and safe pregnancy has been the yearning of almost everyone. Cardiotocography is a technical means of recording the fetal heart rate and uterine contractions of mothers. These signals help in monitoring the fetal distress and also classify the fetal as being safe or unsafe. These time-series signal dataset is preprocessed using interpolation method to remove the missing beats. Inspired by the monumental success of computer vision field, these preprocessed fetal signal data are encoded into images using Grammian Angular Field, representing some temporal correlation between each time point. This proposed work employs data augmentation strategies on the CTH-UHB Intrapartum dataset and these encoded images are fed to a Convolutional Neural Network, a deep learning algorithm. The paper experiments with both Grammian Angular Summation Field and Grammian Angular Difference field and the resultant images are supplied to a pretrained Capsule neural network for training. CNN can successfully capture the spatial and temporal dependencies in an image and the features learnt from this transfer learning network are utilized for classifying the fetal as being normal, abnormal or suspicious. The work achieves a good predictive performance by understanding the sophistication of the images better.