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Please use this identifier to cite or link to this item: http://hdl.handle.net/1812/977

Title: Feature extraction and classification of Cardiotocogram signal to detect fetal condition
Authors: Raseeda Hamzah
Keywords: Cardiotocogram
CTG
Hilbert Huang Transform
HHT
Intrinsic Mode function
IMF
Fetal condition detection
Issue Date: 2010
Publisher: University Malaya
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.
Description: Dissertation (M.C.S.) -- Faculty of Computer Science & Information Technology, University of Malaya, 2010.
URI: http://dspace.fsktm.um.edu.my/handle/1812/977
Appears in Collections:Masters Dissertations: Computer Science

Files in This Item:

File Description SizeFormat
APPENDIX_A.pdfAppendix A59.25 kBAdobe PDFView/Open
BIBLIOGRAPHY.pdfBibliography104 kBAdobe PDFView/Open
CHAPTER_3.pdfChapter 3641.76 kBAdobe PDFView/Open
CHAPTER_6.pdfChapter 639.99 kBAdobe PDFView/Open
APPENDIX_C.pdfAppendix C730.81 kBAdobe PDFView/Open
CHAPTER_2.pdfChapter 2174.24 kBAdobe PDFView/Open
CHAPTER_5.pdfChapter 552.37 kBAdobe PDFView/Open
APPENDIX_B.pdfAppendix B315.29 kBAdobe PDFView/Open
CHAPTER_1.pdfChapter 186.61 kBAdobe PDFView/Open
CHAPTER_4.pdfChapter 4392.2 kBAdobe PDFView/Open
TABLE_OF_CONTENT.pdfTable of Contents135.75 kBAdobe PDFView/Open


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