ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING

Abstract

The World Health Organization (WHO) estimates that over 15 million infants are born before the entire period of pregnancy. Over a million neonatal deaths occurred in 2015 as a result of preterm delivery, which is the prime cause for most deaths among children under the age of five. Although preterm birth has no hereditary link, only 10% of preterm deliveries in high-income settings result in deaths, compared to a mortality rate of up to 90% in low-income nations. When preterm cases are discovered, in addition to enhanced care provided at home for the expecting mother, the growing foetus may also benefit from medication, hospitalization for the duration of the pregnancy, or both. Low-income countries also struggle with a lack of access to a comprehensive healthcare system, which makes it difficult to proactively detect premature births. Machine learning techniques have a great potential to improve this situation by providing an intelligent framework for the detection of critical situations and consequently alerting the individual in cases of anomalies. This will require further follow-up with medical specialists. In this work, we investigate the application of deep learning and machine learning techniques for the analysis of electrohysterogram (EHG) data, which are uterine electrical impulses used to detect preterm deliveries. The TPEHG dataset is used to train a variety of machine learning classifiers, such as Support Vector Machines, Logistic Regression, and Decision Trees, as well as Deep Neural Networks like Convolutional Neural Networks and LSTMs. Additionally, we use plurality voting to build an ensemble of various neural networks and binary classifiers that are trained to classify EHG signals. The ensemble machine learning classifier with five base classifiers produced the best results overall, with an accuracy of 98.99%, sensitivity of 98.3%, and specificity of 97.9% outperforming several state-of-the-art algorithms for preterm birth detection.