Study on Automatic Recognition of Multiple Lung Sounds
Date Issued
2006
Date
2006
Author(s)
Chen, Kuan-Hung
DOI
zh-TW
Abstract
An algorithm of automatic recognition 15 kinds of lung sounds, including normal and adventitious lung sounds, is presented in this thesis. The proposed system utilizes 11025 Hz sampling rate, and integrates wavelet transform and autoregressive model (AR model) for extracting features of lung sounds. As the aspect of wavelet transform, 12 kinds of wavelet basis, including Coiflet-2, Coiflet-3, Coiflet-4, Coiflet-5, Symlet-5, Symlet-6, Symlet-7, Symlet-8, Daubechies-5, Daubechies-6, Daubechies-7 and Daubechies-8, are comprehensively compared in this study. Among 12 wavelet basis, Daubechies-8 has been confirmed to yields best recognition result through comprehensive comparisons. The system also combines autoregressive model as another feature extraction method, and Akaike information criterion is utilized as principle of order determination. The experimental result clearly shows that by combining AR model and wavelet transform, the proposed method performs better than traditional approaches that only used wavelet transform as primary feature extraction method in recognizing lung sounds.
As the aspect of classification, back propagation neural network is been utilized to perform the task of recognizing pattern in lung sounds. It also has been confirmed that the number of neurons in the hidden layer is optimized. The experimental result shows that average recognition rate yielded by the proposed algorithm reaches as high as 85.3%. In addition, other two neural networks, learning vector quantization network and radio basis function network, are compared along with the previous one, which the recognition accuracy yielded by those methods are 90.4 and 93.3%, respectively. Finally, the three neural networks are integrated together as a vote system that can increase the reliability of the proposed system, and the recognition accuracy yielded by the integrated system further improved to 94.4%. Thus, the system can be developed for home care equipment, or an assist for establishing lung sound databases.
Subjects
肺音
小波轉換
自我迴歸模型
類神經網路
lung sound
wavelet transform
AR model
neural network
Type
thesis
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