The Use of Convolutional Neural Networks in Biomedical Data Processing

摘要

In this work, we study the use of convolutional neural networks for biomedical signal processing. Convolutional neural networks show promising results for classifying images when compared to traditional multilayer perceptron, as the latter do not. take spatial structure of the data into an account. Cardiotocography (CTG) is a monitoring of fetal heart rate (FHR) and uterine contractions (UC) used by obstetricians to assess fetal well-being. Because of the complexity of FHR dynamics, regulated by several neurological feedback loops, the visual inspection of FHR remains a difficult task. The application of most guidelines often result in significant inter-and intra-observer variability. Convolutional neural network (CNN, or ConvNet) is inspired by the organization of the animal visual cortex. In the paper we are applying continuous wavelet transform (CWT) to the UC and FHR signals with different levels of time/frequency detail parameter and in two different resolutions. The output 2D structures are fed to convolutional neural network (we are using Tensorflow framework) and we are minimizing the cross entropy function. On the testing dataset (with pH threshold at 7.15) we have achieved the accuracy of 94.1% which is a promising result that needs to be further studied.