Enhancing the Accuracy of Cardiotocogram Analysis using Fuzzy Logic System
Enhancing the Accuracy of Cardiotocogram Analysis using Fuzzy Logic System.
ABSTRACT
Cardiotocogram based monitoring needs a reliable fuzzy logic system to reduce the incidence of unnecessary medical intervention and fetal injury during child labor, due to a high degree of uncertainty and imprecision.
Presently, electronic fetal monitoring is based almost entirely on the Cardiotocogram (CTG), which is a continuous display of the fetal heart rate (FHR) pattern together with the contraction of the womb.
Despite the widespread use of the Cardiotocogram, it has limited significant importance in the fetal outcome as the Cardiotocogram alone does not always provide all the information required to improve the outcome of child labor.
The Cardiotocogram machine and fuzzy system with five patients were used in the analysis to determine the accuracy of fetal heart rate (FHR). Seventy-nine (79) rules were coded into the system to drive the fuzzy inference system.
The aggregation and implication of these rules are explained using surface plots to determine the rule that can fire during the optimization process.
The results show that the Cardiotocogram machine gave an accuracy of ±0.039%, ±0.043%, ±0.047%,±0.048% ±0.053%, while the fuzzy logic system gave an accuracy of ±0.0147%, ±0.0373, ±0.0373%, ±0.0373%, and ±0.0152%, respectively.
The comparison of these results confirmed that the fuzzy logic system has provided a significant method of enhancing the Cardiotocogram analysis with a higher degree of accuracy between 0.6% – 3.8% and makes the system less sensitive to noise or error.
With the results obtained, it is evident that the fuzzy logic system can be used to improve the efficiency of the clinician in making accurate diagnoses.
TABLE OF CONTENTS
Title page …………… i
Approval Page ……. ii
Certification ………. iii
Declaration .. iv
Dedication ……. v
Acknowledgment …….. vi
Abstract …. vii
Table of Contents … viii
List of Figures … xiii
List of Tables ….. xvii
List of Plates ….. xix
List of Acronyms …… xx
CHAPTER ONE: INTRODUCTION
1.1 Background Information………. 1
1.2 Statement of Problem … 3
1.3 Aims and Objectives……… 3
1.4 Significance of the Study. 4
1.5 Scope of Study….. 4
1.6 Thesis Outline…. 5
CHAPTER TWO: LITERATURE REVIEW
2.1 Cardiotocogram (CTG)….. 6
2.2 Cardiotocogram (CTG) Monitoring……. 8
2.2.1 Uncertainties and Imprecision in Fetal Heart Rate and Cardiotocography (CTG) Analysis……….. 8
2.2.2 Improvement in Cardiotocography … 10
2.2.3 Uncertainty in the Real World…….. 11
2.2.3.1 Sources of Uncertainty…….. 11
2.2.3.2 Uncertainty in Data…….. 12
2.2.3.3 Uncertainty in Knowledge……… 12
2.2.3.4 Uncertainty Handling in Expert Systems………… 12
2.2.4 Uncertainty and Imprecision in Management Labour…….. 13
2.2.5 Stan Clinical Guidelines for Action with a Mature Foetus ³ 36 weeks .. 14
2.3 Electronic Fetal Electrocardiogram (ECG) Monitoring…… 15
2.3.1 QRS Detection in Electrocardiogram……… 16
2.3.1.1 Pre-Processing: the Signal Enhancement Scheme for QRS Detection……. 17
2.3.1.2 Non-linear Prediction Filter……. 17
2.3.1.3 Linear Filtering Design by Least Squares Approximation .. 19
2.3.1.4 Comparison of Pre-Processing Techniques…….. 21
2.4 The Fuzzy Logic System .. 22
2.4.1 Fuzzy Set Operators…………. 23
2.4.2 Fuzzy Models for Cardiotocogram and Electrocardiogram Analysis……. 24
2.4.3 Fuzzy State Model for Managing Complex Patterns in Time. 26
2.4.4 Adding Memory………… 27
2.4.5 Using State Machine to Add Memory to Intelligent Systems…… 29
2.5 Review of Related Work ….. 32
CHAPTER THREE: METHODOLOGY
3.1 Materials…… 40
3.1.1 Data Collection………….. 40
3.1.2 Cardiotocogram Machine….40
3.1.3 Ultrasound Machine………… 48
3.1.5 Interfacing Maternal Heart Rate (MHR) with Fetal Heart Rate (FHR) Patterns … 50
3.2 Methods…….. 55
3.3 Fetal Condition Matrix (FCM) ………… 56
3.4 Design of the Fuzzy Model for CTG Analysis……… 57
3.4.1 Fuzzy Logic-Based (FL-B) System Model Design…. 59
4.2 Membership Functions for the Linguistic Variables……. 61
3.4.3 Fuzzy Inference System (FIS) Editor………. 62
3.4.4 Membership Function (MF) Editor…….. 63
3.4.5 Output Membership Function………… 66
3.4.6 Rule Editor…………………………. 66
3.4.7 Rule Viewer……… 67
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Simulation of Results………. 74
4.1.1 Determination of the Optimization Condition………… 75
4.1.2 Analysis of the Optimization Condition…….. 77
4.2 Results Obtained from Cardiotocogram Machine….. 77
4.3 Results Obtained from Ultrasound Machine…………. 78
4.3.1 Comparison of the Accuracy of Cardiotocogram and Ultrasound Machines… …. 78
4.3.2 Determination of the Accuracy of the Measured Values …….. 79
4.3.3 Accuracy of the Measured Values using Cardiotocogram Machine and Fuzzy Inference System…… 79
4.3.4 Determination of the Degree of Uncertainty between Cardiotocogram Machine and Fuzzy Inference System. 80
4.3.5 Accuracy of the Reviewer’s Values using Cardiotocogram Machine and Fuzzy Inference System …….. 81
4.3.6 Determination of the Degree of Uncertainty of the Reviewer’s Values
between Cardiotocogram Machine and Fuzzy Inference System…………. .. 82
4.3.7 Comparison of the Primary Data and Secondary Data…. 83
4.3.8 Graphically Representation of both the Primary Data and Secondary Data…… 84
4.4 Determination of the Surface Plot…………. 85
4.4.1 Analysis of the Surface Plot… 89
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION FOR FUTURE WORK. 91
5.1 Conclusion………. 91
5.1.1 Fuzzy Inference System for Cardiotocogram Machine….. 91
5.2 Recommendation for Future Work………… 92
5.2.1 List of Publications………… 93
References
INTRODUCTION
Childbirth is a critical period for the fetus and the mother. A good outcome of child labor is generally desired but sometimes problems occur that may lead to injury like fetal brain damage, other abnormalities, or even death. Electronic fetal monitoring, introduced by E. J. Quiligan [1] was expected to improve patient care, but this has not yet happened.
The most common monitoring method is based on a continuous trace of the fetal heart rate pattern and maternal contractions, known as the cardiotocograph (CTG). Difficulties in the interpretation of the cardiotocograph have led to unnecessary medical intervention and failure to intervene when necessary may lead to injuries and deaths.
These problems have led to the development of a number of computerized systems to assist with the analysis and interpretation of CTG data. However, despite developments over two decades, there is no significant improvement in fetal outcomes. The progress in computerized cardiotocograph analysis has been impeded by several factors.
There are inherent problems of imprecision and uncertainty in the clinical data and the interpretation methods used. The solutions to this problem are yet to be addressed in the computerized cardiotocograph system. Cardiotocogram does not contain sufficient information accurate for assessment of the fetal condition.
Additional information may be obtained by a proper analysis of changes in the fetal electrocardiogram (ECG), but the problems of uncertainty and imprecision also exist in fetal electrocardiogram analysis [6].
REFERENCES
E.J. Quiligan, The classification of fetal heart rate: II A Revised working classification. Conn.Med. J, Vol. 31, pp779-784, 1967.
J.A. Low, E.J. Karchmar, L. Broekhoven, T. Leonard, M.J. McGrath, S.R. Pancham, and W.N. Piercy. The probability of fetal metabolic acidosis during labour in a population at risk as determined by clinical factors. American Journal Obstetrics Gynaecology, 141:941-951, 1981.
Amer – Wahlin, Hellsten C, Noren H, Hagberg H, Herbst A, Kjellmer I, Lilja H, Lindoff C, Mansson M, Martensson L, Olofsson P. ST analysis of fetal electrocardiogram for intrapatum fetal monitoring: A Swedish randomized controlled trial. Lancet. Aug 18; 358 (9281): 534-8, 2001.
R.D.F. Keith, S. Beckley, J.M. Garibaldi, J.A. Westgate, E.C. Ifeachor and K.R. Greene. A multicentre comparison study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram. British Journal of Obstetrics and Gynaecology, September, Vol. 102, pp688-700, 1995.
SGoncalves H, Rocha AP, Ayres-de-Campos D and Bernades J. Internal versus external intrapatum fetal heart rate monitoring: effect on linear and nonlinear parameters. Physlol Meas, 2006; 27: 307-309.
Chanappa . Bhyri, Kalpana and K.M. Waghmere, Estimation of ECG features using Lab VIEW, International Journal of Computer Science and Communication Technologies, Vol. 2, No.1, 2009, PP. 320- 324.
CSN Team.
