Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6345
標題: 動態頻軸校正之自動喘息音偵測
Automatic Wheeze Detection using Dynamic Frequency Warping
作者: 蔡侃儒
Tsai, Kan-Ru
關鍵字: Dynamic Frequency Warping;動態頻軸校正;Wheeze Detection;喘息偵測
出版社: 電機工程學系所
引用: 中文部份 [1] 林靜幸, 張淑女, 周碧玲, 蘭菊梅, 徐惠禎, 陳瑞娥, 謝春滿, 陳翠芳, 李婉萍, 吳仙妮, 吳書雅, 方莉, 陳玉雲, 孫凡軻, 李業英, 蔡家梅, 曹英, 黃惠滿, “身體檢查與評估指引,” 藝軒, 2009 [2] 黃淑芬, “呼吸照護快速學習,” 合記, 2009 [3] 王小川, “語音訊號處理” 全華, 2005 西文部份 [4] Sandra Reichert, Raymond Gass, Christian Brandt, Emmanuel Andrès, “ Analysis of Respiratory Sounds: State of the Art,” Journal: Citation: Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine 2008:2 45-58. [5] Mazic, J., Sovilj, S. and Magjarevic, R. “Analysis of respiratory sounds in asthmatic infants.” Polytechnic of Dubrovnik, Measurement Science Review, 3:11–21, 2003 [6] H. Pasterkamp, S. S. Kraman and G. R. Wodicka, “Respiratory Sounds Advances Beyond Stethoscope,” American Journal of Respiratory and Critical Care Medicine, vol. 156, pp. 974-987, 1997. [7] S A Taplidou, and U Hadjileontiadis, “Wheeze detection based on timefrequency of breath sounds,” Computer in Biology and Medicine, vol. 37, pp. 1073-1083, 2007. [8] A. Homs-Corbera, D. Salvatella, 1. A. Fiz, 1. Morera, and R. Jane, “Time-frequency characterization of wheezes during forced exhalation,” in Abstr. 5th Conf. Eur. Soc. Eng. Med., Barcelona, Spain, pp. 423 - 424, 1999. [9] S.A. Taplidou, L.J. Hadjileontiadis, T. Penzel, V. Gross, S.M. Panas, “WED: an efficient wheezing-episode detector based on breath sounds spectrogram analysis,” in: Proceedings of the 25th International 43 Conference of the IEEE EMBS, Cancun, Mexico, 2003, pp. 2531–2534. [10] Abhishek Banik, R.S. Anand and M.A. Ansari, “Remote Monitoring and Analysis of Human Lung Sound.” Industrial and Information Systems, IEEE Region 10 and the third international Conference on, 2008. [11] J.C Chien, H.D Wu, F.C Chong, C.I Li, “Wheeze Detection using Cepstral Analysis in Gaussian Mixture Models.” Proceedings of the 29th Annual International Conference of the IEEE EMBS, August 23-26, 2007. [12] J.E. Earis, A.R.A. Sovijarvi, J. Venderschoot, European respiratory society task force report: computerised respiratory sound analysis (CORSA): recommended standards for terms and techniques, Eur. Respir. Rev. 10 (2000) 585–649.
摘要: 
肺音聽診是一個簡單且非侵入式的肺部診斷方法。由醫生藉聽診器來取得患者肺部的呼吸聲音,以判別是否有無異常或額外的聲音,依此診斷出肺部相關的疾病,如氣喘(Asthma)和慢性肺阻塞疾病(Chronic obstructive pulmonary disease,COPD)。然而,由醫生主觀且憑個人經驗來診斷肺部相關的疾病,可能會造成醫療診斷上的錯誤,因此對肺音訊號進行數位化處理以及適當地量化肺音,並根據分析結果來作判斷,將有助於醫生在診斷上更能精確地診斷其病因。
傳統常見的喘息音偵測之演算法,通常是根據喘息音在時域與頻域上的特性,如持續時間的長短(Duration)與出現的頻率範圍來判斷是否為喘息音。而這些用來判斷是否為喘息音的門檻值(Threshold)或準則(Rule)常藉著操作人的經驗法則設定,因此往往會受環境與雜訊等因素的影響,使得結果不如預期。
本研究提出結合動態頻軸校正之自動喘息音偵測的演算法,主要將喘息音位在不同頻帶上的能量調整至同一個頻帶上,以克服喘息音頻率變異所造成的影響。在本論文中,我們將利用子頻帶(Sub-Band)能量當作呼吸聲的特徵參數,再以動態頻軸校正(Dynamic Frequency Warping,DFW)調整呼吸聲之能量頻帶,並根據這些校正過的特徵參數來建立高斯模型(Gaussian Model),最後利用貝氏分類法(Bayesian Classification)來辨識其呼吸聲是否為喘息音。

Nowadays auscultation has been adopted by the physicians as easy, fast and noninvasive way to evaluate and diagnose patients with lung diseases, e.g. asthma (AS) and chronic obstructive pulmonary disease (COPD). Nevertheless, auscultation suffers from subjectivity and variability in the interpretation of its diagnostic information. In order to improve the quality of auscultation, automatic lung sound analysis employing digital signal processing techniques has attracted much attention recently. In this thesis, we will address the problem of automatic wheeze detection which is important for patients with AS. The main idea of the proposed algorithm is to employ the subband energy as the feature parameter and account for the frequency variation of the wheeze sound through dynamic frequency warping. Simulation results demonstrate that the proposed algorithm can achieve 100% wheeze detection for six lung sound databases.
URI: http://hdl.handle.net/11455/6345
其他識別: U0005-1008201114432600
Appears in Collections:電機工程學系所

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