An Automated Lung Sound Preprocessing and Classification System Based OnSpectral Analysis Methods

In this work, respiratory sounds are classified into four classes in the presence of various noises (talking, coughing, motion artefacts, heart and intestinal sounds) using support vector machine classifier with radial basis function kernel. The four classes can be listed as normal, wheeze, crackle and crackle plus wheeze. Crackle and wheeze adventitious sounds have opposite behavior in the time-frequency domain. In order to better represent and resolve the discriminative characteristics of adventitious sounds, non-linear novel spectral feature extraction algorithms are proposed to be employed in four class classification problem. The proposed algorithm, which has achieved 49.86% accuracy on a very challenging and rich dataset, is a promising tool to be used as preprocessor in lung disease decision support systems.

[1]  Hamid Krim,et al.  Persistent Homology of Delay Embeddings and its Application to Wheeze Detection , 2014, IEEE Signal Processing Letters.

[2]  H. Pasterkamp,et al.  Respiratory sounds. Advances beyond the stethoscope. , 1997, American journal of respiratory and critical care medicine.

[3]  Nizamettin Aydin,et al.  Pulmonary crackle detection using time-frequency and time-scale analysis , 2013, Digit. Signal Process..

[4]  Tomasz P. Zielinski,et al.  Joint Application of Audio Spectral Envelope and Tonality Index in an E-Asthma Monitoring System , 2015, IEEE Journal of Biomedical and Health Informatics.

[5]  Zümray Dokur,et al.  Respiratory sound classification by using an incremental supervised neural network , 2009, Pattern Analysis and Applications.

[6]  Gorkem Serbes,et al.  Overcomplete discrete wavelet transform based respiratory sound discrimination with feature and decision level fusion , 2017, Biomed. Signal Process. Control..

[7]  Ivan W. Selesnick,et al.  Sparse signal representations using the tunable Q-factor wavelet transform , 2011, Optical Engineering + Applications.

[8]  A. Bohadana,et al.  Fundamentals of lung auscultation. , 2014, The New England journal of medicine.

[9]  Dirkje S Postma,et al.  The Asthma-COPD Overlap Syndrome. , 2015, The New England journal of medicine.

[10]  N. Malmurugan,et al.  Neural classification of lung sounds using wavelet coefficients , 2004, Comput. Biol. Medicine.

[11]  I. Jolliffe.  Principal Component Analysis and Factor Analysis , 1986 .

[12]  Goutam Saha,et al.  Lung sound classification using cepstral-based statistical features , 2016, Comput. Biol. Medicine.

[13]  Stuart A. Bowyer,et al.  Automatic adventitious respiratory sound analysis: A systematic review , 2017, PloS one.

[14]  P. Piirilä,et al.  Crackles: recording, analysis and clinical significance. , 1995, The European respiratory journal.

[15]  Noam Gavriely,et al.  Breath Sounds Methodology , 1995 .

[16]  Ioanna Chouvarda,et al.  Detection of crackle events using a multi-feature approach , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  Mohammed Bahoura,et al.  Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes , 2009, Comput. Biol. Medicine.

[18]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[19]  Morten Grønnesby,et al.  Machine Learning Based Crackle Detection in Lung Sounds , 2017, ArXiv.

[20]  Ivan W. Selesnick,et al.  Wavelet Transform With Tunable Q-Factor , 2011, IEEE Transactions on Signal Processing.

[21]  Bruno Henrique Groenner Barbosa,et al.  Classification of lung sounds using higher-order statistics: A divide-and-conquer approach , 2016, Comput. Methods Programs Biomed..