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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張璞曾 | |
| dc.contributor.author | Tian-Shiue Yen | en |
| dc.contributor.author | 嚴天斈 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:56:31Z | - |
| dc.date.available | 2015-08-22 | |
| dc.date.copyright | 2011-08-22 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-19 | |
| dc.identifier.citation | [1] Centers for Disease Control and Prevention, Vital Signs, May 2011.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48430 | - |
| dc.description.abstract | 哮鳴音是許多呼吸道疾病診斷的指標，哮鳴音的偵測可以協助醫生對這類患有慢性呼吸道疾病的病患進行長時間的監測，以預防緊急狀況的發生。本文提出一個使用現場可編程邏輯閘陣列(Field Programmable Gate Array, FPGA)的快速哮鳴音偵測可攜式平台，利用FPGA可以硬體加速來達到快速偵測的特性，更可以讓我們的哮鳴偵測系統能夠進一步地製作成系統晶片(System on Chip, SoC)，甚至能與其他生理訊號偵測系統整合成更先進及更複雜的系統晶片。
本系統首先將聲音切割成每兩秒為一個處理單位，並藉由短時傅立葉轉換(Short-Time Fourier Transform, STFT)來求出哮鳴音的時間與頻率上的相關性，並針對時頻圖做雙邊濾波(2D Bilateral Filter)、邊緣偵測(Edge Detection)、多閾值影像分割(Multi-threshold Segementation)、影像形態學處理(Morphological Processing)、以及影像標記(Image Labelling)來萃取出哮鳴音特徵，並以支持向量機(Support Vector Machines)對哮鳴音的特徵進行分類訓練，我們便可以利用訓練好的支持向量機模型對哮鳴音及正常呼吸音進行辨識。 在使用Xilinx ML605開發平台實現後，本系統可以達到51.97MHz的處理速度，偵測系統的效能(Performance)達到0.912，使我們可以在短時間內偵測哮鳴並達到快速監測哮鳴的目的。 | zh_TW |
| dc.description.abstract | Wheezes have often been treated as an important indicator to diagnose the obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to analyze and to long-term monitor the patients’ situations. This thesis proposes a portable wheezing detection system based on Field Programmable Gate Array (FPGA). It accelerates wheezes detection. It could flexibility function as a single process system or be integrated with other biomedical signal detection system.
Firstly, the sound signal is segmented into units of 2 seconds. Then short-time Fourier transform was used to look into the relationship between the time and frequency components of the sound data. Thereafter, we continued processing the spectrogram by 2D bilateral filtering, edge detection, multi-threshold image segmentation, morphological image processing and image labeling to extract the wheezes features according to Computerized Respiratory Sound Analysis (CORSA) standards. Then these features were used to train Support Vector Machines (SVMs) and built the classification models. Finally, this trained model is used to distinguish to detect wheeze for new coming sound data. This system runs on Xilinx ML605 platform. Experiment results show a high performance of 0.912 in analysis of wheeze recognition in hardware. The detection process is good for 51.97 MHz clock frequency. It is good for high speed classification for wheeze. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:56:31Z (GMT). No. of bitstreams: 1 ntu-100-R97945032-1.pdf: 7360466 bytes, checksum: 036cc27c58c54c33519f1e1e3f00f66a (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF FIGURES vii LIST OF TABLES xi CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Objective 2 1.3 Thesis Organization 4 CHAPTER 2 BACKGROUND 5 2.1 Characteristics of the Respiratory Sounds 5 2.1.1 Respiratory Sounds: Normal and Adventitious Sound 6 2.1.2 Main Features of the Wheeze 10 2.2 Related Wheezing Sound Analysis Methods 12 2.2.1 The Continuity of the Peak Checking Methods 12 2.2.2 Feature Extraction and Pattern Recognition Methods 13 2.2.3 Image of the Spectrogram Processing Method 15 2.3 Breath Sound Recording 16 2.3.1 Technique of the Breath Sound Recording 16 2.3.2 Breath Sound Recording System 20 CHAPTER 3 METHODOLOGY 21 3.1 Overall System Architecture 21 3.1.1 Wheezing Detection Algorithm Process Flow 21 3.1.2 SOPC Hardware Architecture 23 3.1.3 MicroBlaze Architecture 24 3.1.4 Processor Local Bus (PLB) 26 3.1.5 Proposed Wheezing Sound Detection System 30 3.1.6 System Validation through Hardware Co-Simulation 34 3.2 Preprocessing Breath Sound Data and STFT 36 3.2.1 Frame Blocking of the Input Breath Sound 36 3.2.2 Introduction of Short–Time Fourier Transform 38 3.2.3 Implementation of STFT 40 3.3 Bilateral Filter 47 3.3.1 Algorithm of the Bilateral Filter 48 3.3.2 Implementation of the Bilateral Filter 50 3.4 Forming Wheezing Mask 63 3.4.1 Basic Ideas of the Multi-threshold Image Segmentation 63 3.4.2 Image Labeling and Image Properties Extraction 65 3.4.3 Implementation of Multi-threshold Image Segmentation 70 3.4.4 Edge Detection 85 3.4.5 Image Closing and Opening 87 3.4.6 Implementation of Wheezing Mask Forming 88 3.5 Support Vector Machines 93 3.5.1 Kernel Methods 93 3.5.2 Support Vector Machines Classification 96 CHAPTER 4 RESULTS AND DISCUSSION 101 4.1 Wheezing Sound Detection Results 101 4.2 Implementation Result of the Wheezing Detection SIP 110 CHAPTER 5 CONCLUSION 114 REFERENCES 115 | |
| dc.language.iso | en | |
| dc.title | 基於FPGA的快速哮鳴音偵測系統 | zh_TW |
| dc.title | An FPGA System for Rapid Wheezing Detection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 林伯星 | |
| dc.contributor.oralexamcommittee | 林志隆,林伯? | |
| dc.subject.keyword | 哮鳴音偵測,現場可編程邏輯閘陣列,FPGA,時頻圖影像處理,支持向量機, | zh_TW |
| dc.subject.keyword | Rapid wheezing detection,Field Programmable Gate Array,FPGA,Spectrogram image processing, Support Vector Machines, | en |
| dc.relation.page | 120 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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