Hardware modeling of algorithm for fetal QRS complex detection using neural network /
Fetal Heart Rate (FHR) monitoring can identify conditions, which may lead to fetal and/or maternal mortality or morbidity. The most familiar means of acquiring the FHR are Doppler ultrasound, Fetal Magneto-cardiogram (FMCG), Superconducting Quantum Interference Device (SQUID) magnetometers and Fetal...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Gombak, Selangor :
Kulliyyah of Engineering, International Islamic University Malaysia,
2009
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| Subjects: | |
| Online Access: | Click here to view 1st 24 pages of the thesis. Members can view fulltext at the specified PCs in the library. |
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| Summary: | Fetal Heart Rate (FHR) monitoring can identify conditions, which may lead to fetal and/or maternal mortality or morbidity. The most familiar means of acquiring the FHR are Doppler ultrasound, Fetal Magneto-cardiogram (FMCG), Superconducting Quantum Interference Device (SQUID) magnetometers and Fetal Phonocardiography (FPCG). The majority of FHR analysis technique is carried out using a bedside monitor over a relatively short period, with the mother-to-be in a recumbent position. FHR abnormalities are unpredictable and may occur at any time. There is still a gap between the existing technologies and the user requirements for a safe, convenient, and reliable FHR monitoring. To monitor the fetus as well as mother during the pregnancy, it needs to focus on long term monitoring. Therefore, this research pays an attention for long term monitoring using Fetal Electrocardiogram (FECG). FECG signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction of the FECG signal from composite abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. In this research, the back-propagation Neural Network (BNN) and Adaptive Linear Neural Network (ADALINE) have been designed to extract and detect the QRS complex of FECG from the abdominal ECG (AECG) to assess the fetus during the pregnancy and labor. It is trained the neural network to recognize the normal waveform and filter out the unnecessary artifacts. The network also needs to consider the existence of noises in the ECG signal, including power line interference, motion artifacts, baseline drift, ECG amplitude modulation with respiration and other composite noises. The performance of the designed algorithm for FHR extraction is 93.75% in resulting. The designed algorithm has been modeled using hardware description language (HDL) for hardware modeling of FHR monitoring system. VHDL (Very High Speed Integrated Circuit Hardware Description Language) has been used to model the algorithm for the target device FPGA (Field Programmable Gate Array) implementation. The designed model has been synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 7.2 Web Edition where the logic utilization was 89% and the DSP block 50%. This research opens up a passage to biomedical researchers, physicians and end users to advocate an excellent understanding of FECG signal and its analysis procedures for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications. |
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| Item Description: | "A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science (Computer and Information Engineering)"--On t.p. |
| Physical Description: | xvi, 156 leaves : ill. ; 30 cm. |
| Bibliography: | Includes bibliographical references (leaves 111-120). |