Modular neural network model based foetal state classification

Cardiotocography (CTG) is a simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) and it is one of the most common diagnostic techniques to evaluate maternal and foetal well-being during pregnancy and before delivery. Assessment of the foetal state can be verified only after delivery using the foetal (newborn) outcome data. One of the most important features defining the abnormal foetal outcome is low birth weight. This paper proposes a multi-class classification algorithm using Modular neural network (MNN) models. It tries to boost two conflicting main objectives of multi-class classifiers: a high correct classification rate level and a high classification rate for each class. Using a Cardiotocography database of normal, suspect and pathological cases, we trained MNN classifiers with 23 real valued diagnostic features collected from total 2126 foetal CTG signal recordings data from UCI Machine Learning Repository. We used the classification in a detection process. The proposed methodology is presented, which then is tested on UCI Cardiotocography unseen testing data sets. Experimental results are promising paving the way for further research in that direction.