International Journal of Engineering Research ISSN:2319-6890)(online),2347-5013(print) Volume No.4, Issue No.2, pp : 60 - 63 01 Feb. 2015 IJER@2015 Page 60 Fuzzy Logic System for Fetal Heart Rate Determination 1 UDO, E.U. and 2 OPARAKU, O.U. 1 Department of Electrical and Electronics Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria. 2 Department of Electronic Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria. 1 thought.umoren@gmail.com , 2 ogbonna.oparaku@unn.edu.ng Abstract-This paper focuses on the Fuzzy Logic System for fetal heart rate determination. The clinical interpretation of fetal heart rate trace is a difficult task and this has led to the development of computerised systems. These systems are limited by their inability to represent uncertainty. This paper describes the development of a fuzzy expert system for fetal heart rate. The fuzzy logic system improved on the crisp system and has achieved the highest overall performance. With the results obtained, it is evident that the fuzzy logic system can be used to improve the efficiency of the clinician position for making accurate diagnosis. Keywords: Fuzzy logic system, uncertainty, fetal heart rate, efficiency. I. Introduction The Cardiotocography (CTG) is regularly monitored in the clinical routine antepartum and during the labour in order to prevent a possible fetal sufferance status. It consists of the simultaneous recording and printout of two signals; the heartbeat frequency of the feotus and the toco signal relative to the uterine contractions. The outcome of labour is usually good for the feotus, however problems may occur that can result in permanent fetal brain damage or even death. Cardiotocogram interpretation is a difficult task requiring clinical experience and significant expertise. Studies have shown that this is often lacking in the clinical setting, with CTG misinterpretation implicated in a large number of preventable fetal deaths and unnecessary interventions [1]. As a result, many computerized systems have been developed to encapsulate expert interpretation of the Cardiotocogram. These range from simple feature extraction and classification systems to intelligent expert systems that assess the CTG along with clinical information to provide management advice [2]. One of the main problems that have impeded progress is the inherent uncertainty in clinical knowledge relating to Cardiotocogram interpretation. This uncertainty has not been effectively represented in any automated Cardiotocogram system. The normal fetal heart rate (FHR) pattern is characterized by a baseline frequency between 110 and 159 beats per minute, presence of periodic accelerations, a normal heart rate variability with a bandwidth between 5 and 25 beats per minute and the absence of decelerations. The FHR pattern is abnormal when the following features are observed. These are the baseline frequency below 110 or above 160 beats per minute, absence of accelerations for more than 45 minutes, absence of FHR variability and late decelerations. A baseline frequency between 100 and 110 can be considered as normal when the duration of pregnancy has exceeded 41 weeks [3]. II. Materials and Methods Fuzzy logic system is the process of formulating the mapping from a given input set to an output set using fuzzy logic. This mapping process provides the basis from which the inference or conclusion can be made. A fuzzy inference process consists of the following five steps:  Fuzzification of input variables  Application of fuzzy operator (AND, OR, NOT) in the IF (antecedent) part of the rule  Implication from the antecedent to the consequent (THEN part of the rule)  Aggregation of the consequents across the rules and Defuzzification. At the top left of the fuzzy inference system, the names of the defined input fuzzy variables are given and at the right of the system, the output variable is shown. The membership functions are located in the boxes and the system name and the Mamdani inference method used are also indicated. The Mamdani-type fuzzy inference, which formulates a mapping from a given input to an output using fuzzy logic, is used as the inference engine [4]. The mapping provides a basis which decisions can be made or patterns recognized. The inference process includes block building, structuring, firing, implication and aggregation of rules [5]. The number of rules is determined by the complexity of the associated fuzzy system. At the lower left of the system, the various steps of the inference process are shown and at the lower right, the name of the input or output variables, its associated MF type, and its range are shown. Figure 1 shows the fuzzy inference system. Fig 1: Fuzzy Inference System