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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張璞曾(Fok-Ching Chong) | |
| dc.contributor.author | Meng-Lun Hsueh | en |
| dc.contributor.author | 薛孟倫 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:51:11Z | - |
| dc.date.available | 2014-02-20 | |
| dc.date.copyright | 2011-02-20 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-02-15 | |
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[2] P. J. Bishop, 'Evolution of the Stethoscope,' Journal of the Royal Society of Medicine, Vol. 73, pp. 448-456, 1980. [3] R.-i. Abdulla, 'The History of the Stethoscope,' Pediatric Cardiology, Vol. 22, pp. 371-372, 2001. [4] S. S. K, Hans Pasterkamp, and George R. Wodicka, 'Respiratory Sounds-Advances Beyond the Stethoscope,' Am. J. Respir. Crit. Care Med. , Vol. 156, pp. 974-987, 1997. [5] J. Vanderschoot, A.R.A. Sovijärvi ,and J.E. Earis, 'Standardization of Computerized Respiratory Sound Analysis,' Eur Respir Rev. Vol. 10, No. 77, pp. 585-645, 2000. [6] G. Charbonneau, N. Meslier, and J-L. Racineux, 'Wheezes,' Eur. Respir. J., Vol. 8, pp. 1942-1948, 1995. [7] M. Bahoura, 'Pattern recognition methods applied to respiratory sounds classification into normal and wheeze Classes, ' Computers in Biology and Medicine, Vol. 39, pp. 824-843, 2009. [8] M. J. Murray, 'Critical care medicine: perioperative management, ' pp.380, Lippincott Williams & Wilkins, 2002. [9] C. Kroen, 'Back to the Bedside: The Art of Physical Diagnosis,' The Hospitalist, Vol. 7, No. 1, (January/February), pp. 22-31, 2003. [10] L. Williams and Wilkins, 'Auscultation Skills: Breath and Heart Sounds,' Springhouse, pp.256,2009. [11] H. Pasterkamp, W. Wiebicke, and R. Fenton, 'Subjective Assessment vs Computer Analysis of Wheezing in Asthma,' Chest, Vol. 91, pp. 376-381, 1987. [12] Bor Shing Lin, et al. 'Wheeze Recognition Based on 2D Bilateral Filtering of Spectrogram,' Biomedical Engineering Applications, Basis and Communications, Vol. 18, pp. 128-137, 2006. [13] R. J. José A. Fiz, et al. 'Detection of Wheezing During Maximal Forced Exhalation in Patients with Obstructed Airways,' Chest, Vol. 122, pp. 186-191, 2002. [14] R.J. Riella, P. Nohama and J. M. Maia, 'Method for automatic detection of wheezing in lung sounds,' Brazilian Journal of Medical and Biological Research, Vol. 42, pp. 674-684, 2009. [15] Antoni Homs-Corbera, et al. 'Time-Frequency Detection and Analysis of Wheezes during Forced Exhalation,' IEEE Transactions on Biomedical Engineering, Vol. 51, No.1, pp. 182-186, 2004. [16] Styliani A. Taplidou, and L. J. Hadjileontiadis, 'Wheeze Detection based on Time-Frequency Analysis of Breath Sounds,' Computers in Biology and Medicine, Vol. 37, pp. 1073-1083, 2007. [17] Sonia Charleston, M. R. Azimi-Sadjadi, and Ramon Gonz´alez-Camarena, 'Interference Cancellation in Respiratory Sounds via a Multiresolution Joint Time-Delay and Signal-Estimation Scheme,' IEEE Transactions on Biomedical Engineering, Vol. 44, No. 10, pp. 1006-1019, October 1997. [18] H. Pasterkamp, I. G. R. Wodicka, and S. S. Kraman, 'Effect of Ambient Respiratory Noise on the Measurement of Lung Sounds,' Medical and Biological Engineering and Computing, Vol. 37, pp. 461-465, 1999. [19] M. Waris, et al. 'A New Method for Automatic Wheeze Detection,' Technology and Health Care, Vol. 6, pp. 33-40, 1998. [20] P. Forgacs, 'Crackles and Wheezes,' The Lancet, Vol. 290, pp. 203-205, July 22, 1967. [21] Anthony Seaton, et al. 'Crofton and Douglas's respiratory diseases,' Vol. 1, Wiley-Blackwell, 2000. [22] C. Y. Cheng, 'Third and Last Breath,' Clinical Vignettes, Vol. 39, No. 3, pp. 16-20, March 2007. [23] Noam Gavriely, et al. 'Measurement and theory of wheezing breath sounds,' J. Appl. Physiol., Vol. 57, pp. 481-492, Aug. 1984. [24] Meng-Lun Hsueh, et al. 'Respiratory Wheeze Detection System,' Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005. [25] Yu-Cheng Wu, et al. 'Reliability Analysis of Lung Sound Recording by Analog-to-Digital Converter and Sound Card,' International Conference on Manufacturing and Engineering Systems, Taipei, Taiwan, 2009. [26] M. R. Portnoff, 'Short-Time Fourier Analysis of Sampled Speech,' IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 29, No. 3, pp. 364-373, June 1981. [27] A. K. Jain, M. N. Murty, and P.J. Flynn, 'Data Clustering: A Review,' ACM Computing Surveys, Vol. 31, No.3, pp. 264-323, September 1999. [28] J. MacQueen, 'Some Methods for Classification and Analysis of Multivariate Observations,' Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California, Vol.1, pp. 281-297, 1967. [29] Shu-Heng Chen, Paul Wang, and Tzu-wen Kuo, 'Computational Intelligence in Economics and Finance,' Vol. 2, Springer, pp.126, 2007. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48282 | - |
| dc.description.abstract | 本論文主要是使用彩色聲譜圖來呈現哮鳴的時頻特徵，並利用k群聚演算法來偵測哮鳴。k群聚演算法主要執行的是分群的工作，其中，群聚組數必須人為先做設定，經過測試後，k值選擇設定成三，三可以在彩色聲譜圖中，對應到紅、綠、藍三個顏色，也可以在聲譜圖上顯示哮鳴。但是k群聚演算法中群組編號的分派是隨機的，因此對應到哮鳴病徵的顏色會產生不固定的現象，本研究利用色彩索引法來修正k群聚演算法，將三個群組編號依照在聲譜圖中所佔的面積比例製作成索引，再將哮鳴索引出來，並標示成紅色。
除此之外，此方法也應用至正常的呼吸音，雜訊消除的效應也一起被探討。結果顯示，經過修正k群聚演算法，不論是正常或哮鳴，訊號-雜訊比都約可提升2dB，同時，色彩索引的製作可以使得哮鳴在彩色聲譜圖上能夠具有穩定的再現性，才能夠提供給醫生，並且能對醫生有幫助。 | zh_TW |
| dc.description.abstract | The aim of this study is to present wheeze time-frequency characteristics in color scales spectrogram, and k-means clustering algorithm is applied to detect wheezes. k-means clustering algorithms are grouped according to its spectrogram nature. The first step is to preset the k value, representing the grouping number. After experiment testing, the k value is set to three. This number corresponds to the color scale spectrogram are red, green, and blue. Wheeze sounds can also be displayed on the spectrogram. However, k-means clustering algorithms group number is assigned randomly. Therefore, the color corresponding to wheezing symptoms has no fixed color. Through the color-indexing method, the wheezing color is set to be red in accordance with the color index production proportions.
In addition, this method is also applied to the normal respiratory sounds, and the effects of noise reduction are discussed. After using modified k-means clustering algorithm, the results show that the signal-to-noise ratios are improved for about 2dB for wheeze and normal cases. The color index can mark wheezing sounds on the color spectrogram in red, and this has a stable representation and reproducibility. This helps the doctor very much in wheeze detection. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:51:11Z (GMT). No. of bitstreams: 1 ntu-100-D91921024-1.pdf: 613051 bytes, checksum: e1a5febee6794517454fb41ffa3fcdba (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | Contents
Abstract i List of Figures ii List of Tables iv Chapter 1 Introduction 1 1.1 Introduction to Respiratory Sounds 1 1.2 Literature Review 5 1.3 Purposes 8 1.3.1 Problem Statement 8 1.3.2 Motivation 9 1.3.3 Expected results 9 1.4 Dissertation organization 11 Chapter 2 Respiratory Wheeze Detection System 12 2.1 The Wheezes Sounds Production Mechanism 12 2.2 System Architecture 14 2.3 Clinical Recording 16 Chapter 3 Methods 17 3.1 Introduction to Analysis Techniques 17 3.2 Signal Processing Procedure 18 3.3 Time-Frequency Analysis Techniques 20 3.4 k-Means Clustering Algorithm 23 3.5 Color Indexing Approach 27 Chapter 4 Results 30 4.1 The options of the time and frequency resolutions 31 4.2 The rearrangement of color distribution 35 4.3 Comparison of Normal and Wheeze Spectrogram 38 4.4 Wheeze Segmentation 41 Chapter 5 Discussion 42 Chapter 6 Conclusion 45 6.1 Contributions 45 6.2 Future work 46 References 47 | |
| dc.language.iso | en | |
| dc.subject | 再現性 | zh_TW |
| dc.subject | 聲譜圖 | zh_TW |
| dc.subject | 哮鳴 | zh_TW |
| dc.subject | k群聚演算法 | zh_TW |
| dc.subject | 色彩索引 | zh_TW |
| dc.subject | 訊號-雜訊比 | zh_TW |
| dc.subject | spectrogram | en |
| dc.subject | reproducibility | en |
| dc.subject | signal-to-noise ratio | en |
| dc.subject | color-indexing | en |
| dc.subject | k-means clustering algorithm | en |
| dc.subject | wheezes | en |
| dc.title | 利用修正k群聚演算法偵測哮鳴 | zh_TW |
| dc.title | Wheeze Detection using Modified k-Means Clustering Algorithm | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 吳惠東(Huey-Dong Wu),盧並裕(Bing-Yuh Lu) | |
| dc.contributor.oralexamcommittee | 陳友倫(Yu-Luen Chen),林耀仁(Yaw-Jen Lin),陸哲駒(Jer-Junn Luh),余松年(Sung-Nien Yu),詹曉龍(Hsiao-Lung Chan),林志隆(Chih-Lung Lin) | |
| dc.subject.keyword | 聲譜圖,哮鳴,k群聚演算法,色彩索引,訊號-雜訊比,再現性, | zh_TW |
| dc.subject.keyword | spectrogram,wheezes,k-means clustering algorithm,color-indexing,signal-to-noise ratio,reproducibility, | en |
| dc.relation.page | 44 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-02-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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