Classification of lung sounds using convolutional neural networks
Tóm tắt
Từ khóa
Tài liệu tham khảo
BDCN Prasadl, PESNK Prasad, Y Sagar, An approach to develop expert systems in medical diagnosis using machine learning algorithms (asthma) and a performance study. IJSC 2(1), 26–33 (2011)
Y Bengio, A Courville, P Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1–31 (2013)
Y Bengio, Learning Deep Architectures for AI. (2009), http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf . Accessed 26 Jan 2016
AB Olshausen, Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)
K Nasrollahi, T Telve, S Escalera, J Gonzalez, TB Moeslund, P Rasti and G Anbarjafari, Spatio-temporal pain recognition in CNN-based super-resolved facial images. In video analytics. Face and facial expression recognition and audience measurement. Third International Workshop, VAAM 2016, and Second International Workshop, FFER 2016, Cancun, Mexico, Revised Selected Papers, Springer, Vol. 10165, pp. 151, December 4, 2016
R Collobert, Deep Learning for Efficient Discriminative Parsing. (2011), http://www.video.lectures.net . Accessed 26 Jan 2016
P Glauner, Comparison of Training Methods for Deep Neural Networks. (2015), https://arxiv.org/abs/1508.06535 . Accessed 26 Jan 2016
L Deng, D Yu, Deep Learning: Methods and Applications. (2014), http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf . Accessed 26 Jan 2016
L Gome, Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts. (2014), http://spectrum.ieee.org/robotics/artificialintelligence/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts . Accessed 26 Jan 2016
Y LeCun, K Kavukcuoglu, C Farabet, Convolutional networks and applications in vision. Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, IEEE, pp. 253–256, 2010
L Deng, Three Classes of Deep Learning Architectures and their Applications: A Tutorial Survey. APSIPA Transactions on Signal and Information Processing, 2012
S Reichert, R Gass, C Brandt, E Andres, Analysis of respiratory sounds state of the art. Clin. Med. 2, 45–58 (2008)
YP Kahya, EC Guler, S Sahin, Respiratory disease diagnosis using lung sounds. Engineering in Medicine and Biology Society, Proceedings of the 19th Annual International Conference of the IEEE, pp. 2051–2053, 1997
Littmann, Digital stethoscope. http://www.littmann.com/wps/portal/3M/en_US/3M-Littmann/stethoscope/stethoscope-catalog/catalog/~/3M-Littmann-Electronic-Stethoscope-Model-3200-Black-Tube-27-inch-3200BK27?N=5932256+4294958300&rt=d . Accessed 26 May 2016
Thinklabs, Digital stethoscope. http://www.thinklabs.com . Accessed 26 May 2016
SH Ah, S Lee, Hierarchical Representation Using NMF Neural Information Processing (Springer Heidelberg, Berlin, 2013)
Acoustics of Speech and Hearing. Spectrograms. UCL/PLS/SPSC2003/WEEK http://www.phon.ucl.ac.uk/courses/spsci/acoustics/week1-10.pdf . Accessed 26 May 2016
H Pasterkamp, SS Kraman, GR Wodicka, Respiratory sounds, advances beyond the stethoscope. Am. J. Respir. Crit. Care Med. 156, 974–987 (1997)
JE Earis, BMG Cheetham, Current methods used for computerized respiratory sound analysis. Eur. Respir. Rev. 10(77), 586–590 (2000)
B Flietstra, N Markuzon, A Vyshedskiy, R Murphy, Automated analysis of crackles in patients with interstitial pulmonary fibrosis. Pulm. Med. 2010, 1–7 (2011)
LR Waitman, KP Clarkson, JA Barwise, PH King, Representation and classification of breath sounds recorded in an intensive care setting using neural networks. J. Clin. Monit. Comput. 16(2), 95–105 (2000)
M Oud, EH Dooijes, JS van der Zee, Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra. IEEE Trans. Biomed. Eng. 47, 1450–1455 (2000)
M Bahoura, C Pelletier, New parameters for respiratory sound classification. Electrical and computer engineering, IEEE CCECE, Canadian Conference. IEEE 3, 1457–1460 (2003)
K.S. Baydar, A. Ertuzun, Y.P. Kahya, Analysis and classification of respiratory sounds by signal coherence method. Engineering in Medicine and Biology Society, Proceedings of the 25th Annual International Conference of the IEEE. IEEE, 2950–2953 (2003)
HG Martinez-Hernandez, CT Aljama-Corrales, R Gonzalez-Camarena, VS Charleston-Villalobos, G Chi-Lem, Computerized classification of normal and abnormal lung sounds by multivariate linear autoregressive model. Engineering in Medicine and Biology Society, IEEE-EMBS, 27th Annual International Conference of the IEEE, pp. 5999–6002, 2006
YP Kahya, M Yeginer, B Bilgic, Classifying respiratory sounds with different feature sets. Conf. Proc. IEEE. Eng. Med. Biol. Soc. 1, 2856–2859 (2006)
S. Alsmadi, Y.P. Kahya, Design of a DSP-based instrument for real-time classification of pulmonary sounds. Comput. Biol. Med. 38, 53–61 (2008)
S. Charleston-Villalobos, G. Martinez-Hernandez, R. Gonzalez-Camarena, G. Chi-Lem, J.G. Carrillo, T. Aljama-Corrales, Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients. Comput. Biol. Med. 41, 473–482 (2011)
M Yamashita, S Matsunaga, S Miyahara, Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration. International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 693–696, 2011
F. Jin, S. Krishnan, F. Sattar, Adventitious sounds identification and extraction using temporal–spectral dominance-based features. IEEE Trans. Biomed. Eng. 58, 3078–3087 (2011)
G Serbes, CO Sakar, YP Kahya, N Aydin, Feature extraction using time–frequency/scale analysis and ensemble of feature sets for crackle detection. 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, pp. 3314–3317, 2011
S Aras, A Gangal, Y Bülbül, Lung sounds classification of healthy and pathologic lung sounds recorded with electronic auscultation. Signal Processing and Communications Applications Conference (SIU), 2015 23th, IEEE, p. 252–255, 2015
C.H. Chen, W.T. Huang, T.H. Tan, C.C. Chang, Y.J. Chang, Using K-nearest neighbor classification to diagnose abnormal lung sounds. Sensors 15, 13132–13158 (2015)
S Rietveld, M Oud, EH Dooijes, Classification of asthmatic breath sounds: preliminary results of the classifying capacity of human examiners versus artificial neural networks. Comput. Biomed. Res. 32(5), 440–448 (1999)
K.E. Forkheim, D. Scuse, H. Pasterkamp, A comparison of neural network models for wheeze detection. WESCANEX 95. Communications, Power, and Computing. Conference Proceedings. IEEE 1, 214–219 (1995)
RJ Riella, P Nohama, JM Maia, method for automatic detection of wheezing in lung sounds. Braz. J. Med. Biol. Res. 42, 674-684 (2009)
A Hashemi, H Arabalibiek, K Agin, Classification of wheeze sounds using wavelets and neural networks. 2011 International Conference on Biomedical Engineering and Technology, IPCBEE, vol.11, IACSIT Press, Singapore, 2011
X Lu, M Bahoura, An integrated automated system for crackles extraction and classification. Biomed. Signal. Process. Contr. 3, 244–254 (2008)
Z Dokur, Respiratory sound classification by using an incremental supervised neural network. Pattern. Anal. Appl. 12, 309–319 (2009)
A Kandaswamy, CS Kumar, RP Ramanathan, S Jayaraman, N Malmurugan, Neural classification of lung sounds using wavelet coefficients. Comput. Biol. Med. 34, 523–537 (2004)
R Folland, E Hines, R Dutta, P Boilot, D Morgan, Comparison of neural network predictors in the classification of tracheal-bronchial breath sounds by respiratory auscultation. Artif. Intell. Med. 31, 211–220 (2004)
RJ Riella, P Nohama, JM Maia, Methodology for Automatic Classification of Adventitious Lung Sounds (Springer, Berlin, Heidelberg/Munich, 2010), pp. 1392–1395
İ Güler, H Polat, U Ergün, Combining neural network and genetic algorithm for prediction of lung sounds. J. Med. Syst. 29, 217–231 (2005)
H Yamamoto, S Matsunaga, K Yamauchi, M Yamashita, S Miyahara, Classification between Normal and Abnormal Respiratory Sounds Based on Maximum Likelihood Approach. Proceedings of 20th International Congress on Acoustics (ICA, Sydney, 2010), pp. 517–520