Combining neural network and genetic algorithm for prediction of lung sounds

被引：69

作者：

Güler I. ^{[1
]}

Polat H. ^{[1
]}

Ergün U. ^{[2
]}

机构：

[1] Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara

[2] Department of Electrical and Electronic Engineering, Faculty of Engineering, Afyon Kocatepe University, Afyon

来源：

Journal of Medical Systems | 2005年 / 29卷 / 3期

关键词：

Auscultation; Genetic algorithm; Lung sounds; MLP; Neural network; Respiratory diseases;

D O I：

10.1007/s10916-005-5182-9

中图分类号：

学科分类号：

摘要：

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks-genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of 15-20 s were sampled from subjects. From each interval, full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated by the spectral analysis are selected by genetic algorithm and applied to neural network. Multilayer perceptron (MLP) neural network employing backpropagation training algorithm was used to predict the presence or absence of adventitious sounds (wheeze and crackle). We used genetic algorithms to search for optimal structure and training parameters of neural network for a better predicting of lung sounds. This application resulted in designing of optimum network structure and, hence reducing the processing load and time. © 2005 Springer Science+Business Media, Inc.

引用

页码：217 / 231

页数：14

共 33 条

[11] Murphy R.L., Gaensler E.A., Holford S.K., Del Bono E.A., Epler G., Crackles in the early detection of asbestosis, Am. Rev. Respir. Dis., 129, pp. 375-379, (1984)
[12] Piirila P., Sovijarvi A.R.A., Kaisla T., Rajala H.M., Katila T., Crackles in patients with fibrosing alveolitis, bronchiectasis, COPD and heart failure, Chest, 99, pp. 1076-1083, (1991)
[13] Sovijarvi A.R.A., Malmberg L.P., Paajanen E., Piirila P., Kallio K., Katila K., Averaged and time-gated spectral analysis of respiratory sounds. Repeatability of spectral parameters in healthy men and in patient with fibrosing alveolitis, Chest, 109, pp. 1283-1290, (1996)
[14] Sovijarvi A.R.A., Piirila P., Luukkonen R., Separation of pulmonary disorders with two-dimensional discriminant analysis of crackles, Clin. Physiol., 16, pp. 172-181, (1996)
[15] Forgacs P., Lung Sounds, (1978)
[16] Sovijarvi A.R.A., Malmberg L.P., Charbonneau G., Vanderschoot J., Dalmasso F., Sacco C., Rossi M., Earis J.E., Characteristics of breath sounds and adventitious respiratory sounds, Eur. Respir. Rev., 10, (2000)
[17] Sovijarvi A.R.A., Vandershoot J., Earis J.E., Computerized Sound Analysis (CORSA): Recommended Standards for Terms and Techniques-ERS Task Force Report, 10, (2000)
[18] Crick F., The recent excitement about neural networks, Nature, 337, pp. 129-132, (1989)
[19] Tu J., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol., 49, pp. 1225-1231, (1996)
[20] Pesu L., Helisto P., Ademovic E., Pesquet J.C., Saarinen A., Sovijarvi A.R.A., Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization, Technol. Health Care, 6, pp. 65-74, (1998)

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