HHT-Based Lung Sound Analysis and Wheezing Recognition

• Main Authors: Chia-Ying Yang, 楊佳穎
• Other Authors: Ren-Guey Lee
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/v24uh7

碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 96 === Auscultation of pulmonary sounds provides valuable clinical information but has been regarded as a tool with low diagnostic value due to the inherent subjectivity in the evaluation of these sounds. And it does not allow a permanent record of data, long-term monitoring of pulmonary sounds in follow-up studies is not possible. For the reasons, the development of algorithms which are used to auto-calculus and self-recognized measures is essential. As a result of the astaticism and nonlinear characteristics of lung sounds signals, a new approach in the analysis of the nonlinear characteristics of wheezes based on Hilbert-Huang Transformation (HHT) was presented in this study. We use the HHT algorithm to estimate the relations between frequency, time, and amplitude about the basic lung sounds signals. The HHT algorithm is used to capable of acquiring, parameterize and subsequently classifying lung sounds into anomalistic and normal sounds with an aim to evaluate them objectively. The sampling rate of 5000 Hz is chosen in this system. We could recognize the wheeze from normal condition by instantaneous frequency(IF) of IMF1 and IMF2（Intrinsic Mode Function, IMF）using Moving Average quantification . The lung sounds of asthmatic patient are converted into analog signals via a commercial stethoscope and can be stored in a commercial recorder. After the pretreatment by filtered signal and sampling processing, we used the HHT algorithm to recognize the part of aberrant voice. In this paper, we bring a not only novel but easier way to keep the transfer function in time-frequency domain of the lung sounds. Experiments show that the method has high accuracy. Furthermore, the proposed method is fully automated without any additional need for adjusting the method to respiratory subjects. In the future we can make a home-care service embedded system and monitoring patients as long as possible.