LINEAR AND NONLINEAR APPROACHES FOR ...

5th International Workshop on Mathematical Methods in Scattering Theory & Biomedical Technology 18 - 19 October 2001, Hotel Kontokali Bay, Corfu

LINEAR AND NONLINEAR APPROACHES FOR CARDIOTOCOGRAM DATA PREDICTION A. Koutras

Electrical and Computer Engineering Dept., University of Patras, 26500 Patras, GREECE I. Christoyianni, G. Nokas, G. Georgoulas, Ch. Stylios1, P. Groumpos

Electrical and Computer Engineering Dept., University of Patras, 26500 Patras, GREECE 1 Computer Engineering Department, University of Ioannina

1. SUMMARY In this paper we present an approach to the Cardiotocogram (CTG) data prediction. Our approach consists of a linear method that uses the Autoregressive model, as well as a number of nonlinear methods that make use of three basic neural network topologies; the multilayer perceptron, the Radial basis function network and the Elman neural network with recurrent structure. Extensive experimental results using various topologies of the neural networks have proved the method’s efficacy when used to forecast future from past values of the CTGs, showing very low error and great accuracy. 2. INTRODUCTION For the past 35 years Electronic Fetal Monitoring (EMF) has been used worldwide for antepatrum and intrapartum fetal surveillance. By the term EMF we almost exclusively mean the continuous recording of fetal heart rate (FHR) and uterine activity (UA), which is also referred as a cardiotocogram. The medical device that is used for acquiring and printing out the corresponding signal is called cardiotocograph. The instantaneous FHR (beats/min) can either be obtained by Doppler ultrasound or directly from the fetal electrocardiogram via scalp electrodes. The uterine activity is measured using an external tocodynamometer or with the use of an intra-uterine pressure catheter (mmHg) [1]. All of nowadays cardiotocographs have a serial port, which allows the interface of the medical tool with a personal computer and the storage of the cardiotocographic data in digitised form in the hard disk of the computer. Today, almost all cardiotocographs in use are operating using Doppler ultrasound with autocorrelation and external tocodynamometer due to the non-invasive nature of this method. Even though the autocorrelation of the signal has spiky “artifacts” (abrupt changes by more than 35 bpm) and also sometimes the rate is halved or doubled [2] or even zeroed when the fetus’ heart is moved outside the range of the transducer. Thus the fetal heart rate is a noisy signal. Using the cardiotocogram (CTG) is common for routine fetal monitoring. The CTG consists of fetal heartbeat and uterine contraction signals. At the site under investigation, such signals have been recorded and stored for further analysis. Usually, the heart rate is pre-processed before it is analyzed. The overall aim is the development of an intelligent alarm system, which can be employed as a tool for decision support. The first step when processing the

5th International Workshop on Mathematical Methods in Scattering Theory & Biomedical Technology 18 - 19 October 2001, Hotel Kontokali Bay, Corfu

given data sets is to detect the artifacts, so that they can be removed. In order to improve the detection of artifacts, the next value in the time series can be forecast and compared with the actual value. Values that deviate considerably from the forecast are more likely to be disturbed by measuring errors than those, which are close to the forecast ones [4]. As proposed in [3], such a forecasting system could also be used for repairing the input signals. Instead of replacing missing values by average or preceding ones, they could be replaced by the forecast values, which are more likely to resemble the true values. The problem of predicting the future on the basis of some collected historical data arises in many scientific, economic and engineering applications [5-7], i.e. the prediction of future sample value of a time series by extracting knowledge from its past values. The most powerful approach to the problem of prediction is to find a law underlying the given dynamic process or phenomenon. If such law can be discovered and analytically described, i.e. by a set of ordinary differential equations, then by solving them we can predict the future if the initial conditions are completely specified. Unfortunately, the information about a dynamic process under investigation is often only partial and incomplete, so the prediction cannot be based on a known analytical model. In this case we try a less powerful approach and attempt to discover some strong empirical regularities in the observation of the time series. The unknown dynamic process is described by the nonlinear multivariable function y (k )  F  y (k  1), y(k  2),  y (k  n)

(1)

where y(k) (k=N, N-1, …, n) with n