Chapter 7 - Recurrent Neural Networks in Medical Data Analysis and Classifications

Abstract

This chapter discusses dynamical neural network architectures for the classification of medical data. Various researches have indicated that recurrent neural networks such as the Elman network demonstrated significant improvements when used for pattern recognition in medical time-series data analysis and have obtained high accuracy in the classification of medical signals. The aim of this chapter is to provide a literature survey of various applications of dynamical neural networks in medical-related problems. Medical signals recorded in various applications contain noise that could result from measurement error or due to recording tools. Therefore, this chapter will discuss how data preprocessing can be used to extract the features and remove the noises. A case study using the Elman, Jordan, and Layer recurrent networks for the classification of uterine electrohysterography signals for the prediction of term and preterm delivery for pregnant women is also presented.