Automated classification of lung sound signals based on empirical mode decomposition

Abstract Chronic obstructive pulmonary disease (COPD) is a chronic non-reversible lung disease. Other acute respiratory illness due to infections are termed as non-chronic. In general, the pulmonologist carries preliminary screening by accessing lung sounds. In this paper, we propose a methodology to automatically classify lung sounds associated with non-chronic and chronic categories. To accomplish the task, at first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). Considering the availability of IMFs in entire dataset and by using a hybrid strategy, first four IMFs have been selected for feature extraction purpose. The IMFs are then further processed to construct two-dimensional (2D) and higher-dimensional (HD) phase space representation (PSR). The feature space includes the 95% confidence ellipse area from 2D-PSR and interquartile range (IQR), mean, median, standard deviation, skewness and kurtosis of Euclidian distances computed from HD-PSR. The process is carried out for the first four IMFs corresponding to the non-chronic and chronic categories of the lung sounds. Neighborhood component analysis is used to select best performing features. The computed and selected features depict a significant ability to discriminate the two categories of lung sound signals. To perform classification, we use ensemble classifiers. Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying non-chronic and chronic lung sound signals. Ensemble of Bagged tree provides the highest classification accuracy of 97.14% over feature space constituted by 10D-PSR of fourth IMF.

[1]  L. Bacharier,et al.  Diagnosis and management of early asthma in preschool-aged children. , 2012, The Journal of allergy and clinical immunology.

[2]  Varun Bajaj,et al.  Convolutional neural networks based efficient approach for classification of lung diseases , 2019, Health Information Science and Systems.

[3]  Kexin Zhang,et al.  Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network , 2019, 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

[4]  Achmad Rizal,et al.  Entropy measurement as features extraction in automatic lung sound classification , 2017, 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC).

[5]  Wen-Liang Hwang,et al.  EMD Revisited: A New Understanding of the Envelope and Resolving the Mode-Mixing Problem in AM-FM Signals , 2012, IEEE Transactions on Signal Processing.

[6]  Walker Hk,et al.  Peripheral Blood Smear -- Clinical Methods: The History, Physical, and Laboratory Examinations , 1990 .

[7]  Novruz Allahverdi,et al.  Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease , 2020, IEEE Journal of Biomedical and Health Informatics.

[8]  Rajeev Sharma,et al.  Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions , 2015, Expert Syst. Appl..

[9]  Ram Bilas Pachori,et al.  Variable cosine windowing of intrinsic mode functions: Application to gear fault diagnosis , 2012 .

[10]  Ram Bilas Pachori,et al.  Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions , 2014, Comput. Methods Programs Biomed..

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

[12]  Ali Gangal,et al.  Comparison of different features derived from mel frequency cepstrum coefficients for classification of single channel lung sounds , 2017, 2017 40th International Conference on Telecommunications and Signal Processing (TSP).

[13]  Muhammad Umar Khan,et al.  An Automated System towards Diagnosis of Pneumonia using Pulmonary Auscultations , 2019, 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS).

[14]  N. P. Jawarkar,et al.  Cell Phone Based Remote Early Detection of Respiratory Disorders for Rural Children Using Modified Stethoscope , 2012, 2012 International Conference on Communication Systems and Network Technologies.

[15]  Madhuchhanda Mitra,et al.  Empirical mode decomposition based ECG enhancement and QRS detection , 2012, Comput. Biol. Medicine.

[16]  Wei Yang,et al.  Neighborhood Component Feature Selection for High-Dimensional Data , 2012, J. Comput..

[17]  J. Piskorski,et al.  Geometry of the Poincaré plot of RR intervals and its asymmetry in healthy adults , 2007, Physiological measurement.

[18]  D. S. Morillo,et al.  Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[19]  Jae-Kwon Kim,et al.  Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance , 2014, Comput. Methods Programs Biomed..

[20]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[21]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[22]  Roger W. Johnson,et al.  An Introduction to the Bootstrap , 2001 .

[23]  José Antonio Fiz,et al.  Automatic Differentiation of Normal and Continuous Adventitious Respiratory Sounds Using Ensemble Empirical Mode Decomposition and Instantaneous Frequency , 2016, IEEE Journal of Biomedical and Health Informatics.

[24]  Said Gaci,et al.  A New Ensemble Empirical Mode Decomposition (EEMD) Denoising Method for Seismic Signals , 2016 .

[25]  Sengul Dogan,et al.  A novel ternary and signum kernelled linear hexadecimal pattern and hybrid feature selection based environmental sound classification method , 2020 .

[26]  Sengul Dogan,et al.  An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector , 2020, Biomedical Signal Processing and Control.

[27]  Klaus D. McDonald-Maier,et al.  Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition , 2017, Comput. Biol. Medicine.

[28]  Hui Wang,et al.  An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[29]  Peter Hult,et al.  Chaotic dynamics of respiratory sounds , 2006 .

[30]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[31]  David J. Hewson,et al.  Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[32]  Suresh N. Mali,et al.  Classifier Ensemble Design for Imbalanced Data Classification: A Hybrid Approach☆ , 2016 .

[33]  Rajkumar Palaniappan,et al.  Respiratory sound classification using cepstral features and support vector machine , 2013, 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS).

[34]  E. H. Dooijes,et al.  Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra , 2000, IEEE Transactions on Biomedical Engineering.

[35]  Gulay Tezel,et al.  Comparision of LPC Based Parametric Techniques for Respiratory Sounds Recognition , 2018 .

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

[38]  Ahmad Taher Azar,et al.  Performance analysis of support vector machines classifiers in breast cancer mammography recognition , 2013, Neural Computing and Applications.

[39]  Liu Xiao,et al.  BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification , 2016 .

[40]  P. King.  The pathophysiology of bronchiectasis , 2009, International journal of chronic obstructive pulmonary disease.

[41]  Office on Smoking.  How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General , 2010 .

[42]  P Nohama,et al.  Method for automatic detection of wheezing in lung sounds. , 2009, Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas.

[43]  M. Jagannath,et al.  Advances in biomedical signal and image processing – A systematic review , 2017 .

[44]  Hai Huang,et al.  Speech pitch determination based on Hilbert-Huang transform , 2006, Signal Process..

[45]  Ram Bilas Pachori,et al.  Biomedical Engineering Fundamentals , 2020, Intelligent Internet of Things.

[46]  Zhizhong Wang,et al.  Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis , 2006, Comput. Methods Programs Biomed..

[47]  Andrey Vyshedskiy,et al.  Automated lung sound analysis in patients with pneumonia. , 2004, Respiratory care.

[48]  W. J. Williams,et al.  Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures , 2005, Brain Topography.

[49]  Xiaochen Li,et al.  Trends and risk factors of mortality and disability adjusted life years for chronic respiratory diseases from 1990 to 2017: systematic analysis for the Global Burden of Disease Study 2017 , 2020, BMJ.

[50]  Sengul Dogan,et al.  A novel octopus based Parkinson’s disease and gender recognition method using vowels , 2019 .

[51]  Rajeev Sharma,et al.  Classification of Normal and Epileptic Seizure EEG Signals Based on Empirical Mode Decomposition , 2015, Complex System Modelling and Control Through Intelligent Soft Computations.

[52]  Nuno M. Fonseca Ferreira,et al.  Classification of Images of Childhood Pneumonia using Convolutional Neural Networks , 2019, BIOIMAGING.

[53]  Andrea Tagarelli,et al.  Deep Auscultation: Predicting Respiratory Anomalies and Diseases via Recurrent Neural Networks , 2019, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS).

[54]  E. Andrès,et al.  Analysis of Respiratory Sounds: State of the Art , 2008, Clinical medicine. Circulatory, respiratory and pulmonary medicine.

[55]  Ki H. Chon,et al.  Tidal Volume Estimation Using the Blanket Fractal Dimension of the Tracheal Sounds Acquired by Smartphone , 2015, Sensors.

[56]  Sengul Dogan,et al.  A novel local senary pattern based epilepsy diagnosis system using EEG signals , 2019, Australasian Physical & Engineering Sciences in Medicine.

[57]  T.E. Prieto,et al.  Measures of postural steadiness: differences between healthy young and elderly adults , 1996, IEEE Transactions on Biomedical Engineering.

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

[59]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[60]  Yan Shi,et al.  A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network , 2019, International journal of biological sciences.

[61]  Aydin Akan,et al.  Epileptic seizure classifications using empirical mode decomposition and its derivative , 2020, BioMedical Engineering OnLine.

[62]  Paul H. King,et al.  Representation and Classification of Breath Sounds Recorded in an Intensive Care Setting Using Neural Networks , 2004, Journal of Clinical Monitoring and Computing.

[63]  Adriano O Andrade,et al.  Study of age-related changes in postural control during quiet standing through Linear Discriminant Analysis , 2009, Biomedical engineering online.

[64]  Sengul Dogan,et al.  A novel spiral pattern and 2D M4 pooling based environmental sound classification method , 2020 .

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

[66]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[67]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[68]  Ioanna Chouvarda,et al.  An open access database for the evaluation of respiratory sound classification algorithms , 2019, Physiological measurement.

[69]  Yoon Sang Lee,et al.  Ensemble Classification Method for Imbalanced Data Using Deep Learning , 2018, WEB.

[70]  Achmad Rizal,et al.  Fractal Dimension for Lung Sound Classification in Multiscale Scheme , 2018, J. Comput. Sci..

[71]  Maolin Cai,et al.  A Novel Method for Automatic Identification of Breathing State , 2019, Scientific Reports.