Feature extraction and classification of Cardiotocogram signal to detect fetal condition

Hamzah, Raseeda (2010) Feature extraction and classification of Cardiotocogram signal to detect fetal condition. Masters thesis, University of Malaya.

[img] PDF
APPENDIX_A.pdf

Download (60kB)
[img] PDF
BIBLIOGRAPHY.pdf

Download (106kB)
[img] PDF
CHAPTER_3.pdf

Download (657kB)
[img] PDF
CHAPTER_6.pdf

Download (40kB)
[img] PDF
APPENDIX_C.pdf

Download (748kB)
[img] PDF
CHAPTER_2.pdf

Download (178kB)
[img] PDF
CHAPTER_5.pdf

Download (53kB)
[img] PDF
APPENDIX_B.pdf

Download (322kB)
[img] PDF
CHAPTER_1.pdf

Download (88kB)
[img] PDF
CHAPTER_4.pdf

Download (-1B)
[img] PDF
TABLE_OF_CONTENT.pdf

Download (-1B)

Abstract

ABSTRACT Cardiotocogram (CTG) is a machine to detect fetal condition in mother’s abdomen by attaching electrode to mother’s abdomen surface. The fetal condition would be diagnosed by an expert based on the CTG signal. However there is a need for a computer based analysis especially when the doctors are not available or in condition when the monitoring and analysis need to be done continuously. Therefore CTG software may help in assisting the initial diagnosis for fetal’s condition (normal or abnormal). Hilbert Huang Transform (HHT) is used in this project because of the capability in analysing nonlinear and nonstationary signal. It is also able to generate Intrinsic Mode function (IMF) which can provide meaningful information of the signal. The classification is done by using Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN). The aim of the classification is to differentiate between two groups of CTG signal. In this project, engine development and simulation is using Matlab software. A total of 110 datasets (CTG signal)gathered from Hospital Universiti Kebangsaan Malaysia are used in this project to test the efficiency of the engine. In comparison between LDA and ANN, the classification performance shows that LDA is better. LDA had achieved up to 96% correct classification while ANN has achieved 75% correct classification. ABSTRAK Kardiotokogram adalah sebuah mesin yang mengesan keadaan bayi dalam kandungan ibu dengan melekatkan wayar pengesan kepada permukaan perut. Keadaan bayi akan di analisa oleh pakar melalui isyarat mesin KTG. Walaubagaimanapun, analisa berasaskan computer diperlukan apabila ketiadaan pakar atau dalam keadaan pemantauan dan analisa perlu dilakukan berterusan. Oleh itu perisian KTG boleh membantu dalam analisa awal keadaan bayi. Penukar Huang Hilbert digunakan dalam projek ini kerana kebolehannya dalam menganalisa data tidak linear dan tidak static. Ia juga berkebolehan menghasilkan Fungsi Keadaan Intrinsik yang memberi informasi berguna Pengkelasan dilakukan menggunakan Analisis Pembeza Layan Linear dan Jaringan Saraf uatan. Tujuan pengelasan adalah bagi membezakan antara 2 kelas bagi isyarat KTG. Dalam projek ini, pembangunan enjin dan simulasi adalah menggunakan perisian Matlab. Sejumlah 110 set data (Isyarat KTG) dikumpulkan dari Hospital Universiti Kebangsaan Malaysia digunakan dalam projek ini untuk menguji kecekapan enjin. Dalam perbandingan antara LDA dan ANN, prestasi pengelasan menunjukkan LDA lebih baik. LDA telah mencapai sehingga 96% klasifikasi betul manakala ANN telah mencapai 75% klasifikasi betul.

Item Type: Thesis (Masters)
Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Depositing User: MS NOOR ZAKIRA ZULRIMI
Date Deposited: 16 Jul 2013 04:01
Last Modified: 16 Jul 2013 04:01
URI: http://repository.um.edu.my/id/eprint/461

Actions (login required)

View Item View Item