Computer-Aided Diagnosis System for Chronic Obstructive Pulmonary Disease Using Empirical Wavelet Transform on Auscultation Sounds

In this study, it is aimed to develop computer-aided a diagnosis system for Chronic Obstructive Pulmonary Disease (COPD) which is a completely incurable and chronic disease. The COPD causes obstructions of the airways in the lungs by arising air pollution environments. Contributing analysis of abnormalities in simple ways is very important to shorten the duration of treatment by early diagnosis. The most common diagnostic method for respiratory disorders is auscultation sounds. These sounds are also essential and effective signals for diagnosing the COPD. The analysis was performed using signals from the RespiratoryDatabase@TR which consists of 12-channel lung sounds. In the computerized analysis, Empirical Wavelet Transform (EWT) algorithm was applied to the signals for extracting different modes. Afterwards the statistical features were extracted from each EWT modulation. The highest classification performances were achieved with the rates of 90.41%, 95.28%, 90.56% and 85.78% for Support Vector Machine, AdaBoost, Random Forest and J48 Decision Tree, respectively. The contribution of the study is reducing the diagnosis time to 5 seconds within higher accuracy rate.

[1]  Nicolás Ruiz-Reyes,et al.  A novel wheezing detection approach based on constrained non-negative matrix factorization , 2019, Applied Acoustics.

[2]  Semra Içer,et al.  Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds , 2014, Digit. Signal Process..

[3]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[4]  Wei Liu,et al.  Seismic Time–Frequency Analysis via Empirical Wavelet Transform , 2016, IEEE Geoscience and Remote Sensing Letters.

[5]  B. McManus,et al.  Biomarker Development for Chronic Obstructive Pulmonary Disease. From Discovery to Clinical Implementation. , 2015, American journal of respiratory and critical care medicine.

[6]  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.

[7]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[8]  Piyush Kumar Shukla,et al.  Deep Transfer Learning Based Classification Model for COVID-19 Disease , 2020, IRBM.

[9]  M. A. Fernandez-Granero,et al.  An artificial intelligence approach to early predict symptom-based exacerbations of COPD , 2018 .

[10]  Shu-Ting Zhu,et al.  Utility of Point-of-Care Lung Ultrasound for Clinical Classification of COVID-19 , 2020, Ultrasound in Medicine & Biology.

[11]  Pedro L. Melo,et al.  Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[12]  Su Ruan,et al.  Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation , 2020, Computers in Biology and Medicine.

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

[14]  M. S. Swapna,et al.  Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation , 2020, Chaos, Solitons & Fractals.

[15]  Antoine Tahan,et al.  A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis , 2016 .

[16]  Hojjat Adeli,et al.  A new music-empirical wavelet transform methodology for time-frequency analysis of noisy nonlinear and non-stationary signals , 2015, Digit. Signal Process..

[17]  Thomas Song,et al.  Interobserver and Intraobserver Variability in the CT Assessment of COVID-19 Based on RSNA Consensus Classification Categories , 2020, Academic Radiology.

[18]  Jinglong Chen,et al.  Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment , 2016 .

[19]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

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

[21]  Ashok Mondal,et al.  Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm , 2017, Comput. Methods Programs Biomed..

[22]  Kenneth Sundaraj,et al.  Identification of asthma severity levels through wheeze sound characterization and classification using integrated power features , 2019, Biomed. Signal Process. Control..

[23]  Yang Zhang,et al.  8-Hydroxy-2’-deoxyguanosine as a biomarker of oxidative stress in acute exacerbation of chronic obstructive pulmonary disease , 2019, Turkish journal of medical sciences.

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

[25]  D. Maharaja,et al.  Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification From Fundus Image , 2017 .

[26]  M. Usman Akram,et al.  Crackle separation and classification from normal Respiratory sounds using Gaussian Mixture Model , 2015, 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS).

[27]  Goutam Saha,et al.  Multichannel lung sound analysis for asthma detection , 2018, Comput. Methods Programs Biomed..

[28]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[29]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[30]  Gokhan Altan,et al.  Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays , 2017, ArXiv.

[31]  Wee Ser,et al.  A robust audio classification system for detecting pulmonary edema , 2018, Biomed. Signal Process. Control..

[32]  Trapti Jain,et al.  Estimation of Single-Phase and Three-Phase Power-Quality Indices Using Empirical Wavelet Transform , 2015, IEEE Transactions on Power Delivery.

[33]  Gokhan Altan,et al.  The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds , 2019, Journal of Intelligent Systems with Applications.

[34]  Yakup Kutlu,et al.  Deep learning with 3D-second order difference plot on respiratory sounds , 2018, Biomed. Signal Process. Control..

[35]  Mohammad Hassan Moradi,et al.  The attractor recurrent neural network based on fuzzy functions: An effective model for the classification of lung abnormalities , 2017, Comput. Biol. Medicine.

[36]  S. Altın,et al.  The relationship between serum levels of surfactant protein D in COPD exacerbation severity and mortality , 2019, Turkish journal of medical sciences.