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  • 學位論文

建立於隨身聽的數位式電子聽診器

A digital electronic stethoscope based on an audio player

指導教授 : 蔡章仁
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摘要


人體內部有許多的器官,不同的器官同時運作以維持人體的正常運作;肺臟為提供細胞充足氧氣並排放出人體不需要之二氧化碳的重要器官。氣體流通在肺臟時會因為與氣管壁摩擦而產生肺音;而當肺臟有病變時,肺音亦會跟著改變。氣喘是一種慢性支氣管炎,在肺音訊號上以喘鳴音為最典型的特徵,另外氣喘在肺音頻率上所造成的變化也是很重要的參考指標。 傳統聽診器無法保留患者的肺音訊號,因而無法對訊號做更進一步的分析;而目前市售之數位化電子式聽診器有著單價偏高及錄音組數有限等缺點。因此本研究的主要目的,為使用市售的數位隨身聽作為基礎,開發一建立於隨身聽的數位化電子式聽診器;包括了內含電容式麥克風的聽診頭、放大濾波電路、儲存數位化肺音訊號的隨身聽。軟體部分則利用C語言撰寫數位隨身聽的類8051處理器,使用快速傅立葉轉換及功率頻譜密度等分析方法作喘鳴音的偵測。

關鍵字

喘鳴音 數位隨身聽 肺音

並列摘要


There are many organs inside a human body. They operate together to keep a person alive. The lung is an important organ to transport oxygen into the body and export carbon dioxide outside of the body. As air hits the walls of these airways, turbulence is created and produces sound, that is what we called lung sound. But if you have some diseases in your respiratory system, lung sounds may change. Asthma is a kind of bronchitis, this will produce a particular sound called wheeze. Besides that, the influence on frequency analysis on lung sound caused by asthma is also an important resource in detecting wheeze. Lung sound signals can’t be saved using an acoustics stethoscope, and also can’t be reused. There are some digitized electronic stethoscopes, but they are usually expensive and can’t store too many lung sound files. So the purpose of this study is to produce a digital electronic stethoscope based on a digital audio player. It contains an electric condenser microphone, an amplifier circuit, a filter circuit, and a storage device. The system contains a diagnosis program which is written in C language on a 8051 processor. It uses FFT and PSD to analyze the lung sound signals in order to detect wheeze.

並列關鍵字

Wheeze Lung sound Digital audio player

參考文獻


[1] L. A. Geddes, “Birth of the stethoscope,” IEEE Engineering in Medicine and Biology Magazine, vol. 24, Issue 1, 84-86, 2005.
[4] K. E. Forkheim, D. Scuse, H. Pasterkamp, “A comparison of neural network models for wheeze detection,” IEEE Communications, Power, and Computing Conference Proceedings, vol. 1, 214-219, 1995.
[5] R. J. Riella, P. Nohama, R. F. Borges, A. L. Stelle, “Automatic wheezing recognition in recorded lung sounds,” Proceedings of the Annual International Conference of the IEEE EMBS, vol. 3, 2535-2538, 2003.
[6] A. Homs-Corbera, J. A. Fiz, J. Morera, R. Jane, “Time-frequency detection and analysis of wheezes during forced exhalation,” IEEE transactions on biomedical engineering, vol. 51, 182-186, 2004.
[7] B. -S. Lin, B. -S. Lin, H. -D. Wu, F. -C. Chong, S. -J. Chen, “Wheeze recognition based on 2D bilateral filtering of spectrogram,” Biomed. Eng. Appl. Basis Comm., vol. 18, 128-137, 2006.

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