Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds

Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases which cannot be treated but can be kept under control in certain stages. COPD has five severities, including at-risk, mild, moderate, severe, and very severe stages. Diagnosis of COPD at early stages needs additional clinical tests for even experienced specialists. The study aims at detecting the severity of the COPD to start treatment for preventing the progression of the disease to the next levels. We analyzed 12-channel lung sounds with different COPD severities from RespiratoryDatabase@TR. The lung sounds were recorded from the clinical auscultation points from 41 patients on posterior (chest) and anterior (back) sides. 3D second-order difference plot was applied to extract characteristic abnormalities on lung sounds. Cuboid and octant-based quantizations were utilized to extract characteristic abnormalities on chaos plot. Deep extreme learning machines classifier (deep ELM), which is one of the most stable and fast deep learning algorithms, was utilized in the classification stage. Novel HessELM and LuELM autoencoder kernels were adapted to deep ELM and reached higher generalization capabilities with a faster training speed against the conventional ELM autoencoder. The proposed deep ELM model with LuELM autoecoder has separated five COPD severities with classification performance rates of 94.31%, 94.28%, 98.76%, and 0.9659 for overall accuracy, weighted-sensitivity, weighted-specificity, and area under the curve (AUC) value, respectively. The proposed deep analysis of 12-channel lung sounds provides a standardized and entire lung assessment for identification of COPD severity. Our study is a pioneering approach that directly focuses on lung sounds. Novel deep ELM kernels have performed a higher generalization and fast training compared to conventional kernels.

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1] Celli BR, MacNee W, Agusti A, Anzueto A, Berg B et al. Standards for the diagnosis and treatment of patients with COPD: A summary of the ATS/ERS position paper. European Respiratory Journal 2004; 23: 932-946. doi: 10.1183/09031936.04.00014304
[2] Gunen H, Hacievliyagil SS, Yetkin O, Gulbas G, Mutlu LC et al. Prevalence of COPD: First epidemiolog- ical study of a large region in Turkey. European Journal of Internal Medicine 2008; 19(7): 499-504. doi: 10.1016/j.ejim.2007.06.028
[3] Roisin RR. Chronic obstructive pulmonary disease updated 2010 global initiative for chronic obstructive lung disease. Global Initiative for Chronic Obstructive Lung Disease 2016; 1: 1-94. doi: 10.1097/00008483-200207000-00004
[4] Meslier N, Charbonneau G, Racineux JL. Wheezes. European Respiratory Journal 1995: 8(11): 1942-8. doi: 10.1183/09031936.95.08111942
[5] Sanchez-Morillo D, Leon Jimenez A, Moreno SA. Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease. Journal of the American Medical Informatics Association 2013; 20.e1: 111-117. doi: 10.1136/amiajnl-2012-001171
[6] Altan G, Kutlu Y, Pekmezci AO, Nural S. Deep learning with 3D-second order difference plot on respiratory sounds. Biomedical Signal Processing and Control 2018; 45: 58-69. doi: 10.1016/j.bspc.2018.05.014
7] Naves R, Barbosa BHG, Ferreira DD. Classification of lung sounds using higher-order statistics: A divide-and-conquer approach. Computer Methods and Programs in Biomedicine 2016; 129: 12-20. doi: 10.1016/j.cmpb.2016.02.013
[8] Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A. An artificial intelligence approach to early predict symptom-based exacerbations of COPD. Biotechnology and Biotechnological Equipment 2018; 32(3): 778-784. doi: 10.1080/13102818.2018.1437568
[9] Oweis R, Abdulhay E, Khayal A, Awad A. An alternative respiratory sounds classification system utilizing artificial neural networks. Biomedical Journal 2014; 38(2): 153-161. doi: 10.4103/2319-4170.137773
[10] Kanwade A, Bairagi V. Classification of COPD and normal lung airways using feature extraction of electromyo- graphic signals. Journal of King Saud University-Computer and Information Sciences 2019; 31(4): 506-513. doi: 10.1016/j.jksuci.2017.05.006
[11] Newandee DA, Reisman SS, Bartels AN, De Meersman RE. COPD severity classification using principal component and cluster analysis on HRV parameters. In: Proceedings of the IEEE Annual Northeast Bioengineering Conference; Newark, NJ, USA; 2003. pp. 134-135. doi: 10.1109/NEBC.2003.1216028
[12] Hooda R, Mittal A, Sofat S. Lung segmentation in chest radiographs using fully convolutional networks. Turkish Journal of Electrical Engineering & Computer Sciences 2019: 27: 710-722. doi: 10.3906/elk-1710-157
[13] Altan G, Kutlu Y, Allahverdi N. Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE Journal of Biomedical and Health Informatics 2020; 24(5): 1344-1350. doi: 10.1109/JBHI.2019.2931395
[14] Yu W, Zhuang F, He Q, Shi Z. Learning deep representations via extreme learning machines. Neurocomputing 2015; 149-Part A: 308-315. doi: 10.1016/j.neucom.2014.03.077
[15] Tissera MD, McDonnell MD. Deep extreme learning machines: supervised autoencoding architecture for classifica- tion. Neurocomputing 2016; 174-Part A: 42-49. doi: 10.1016/j.neucom.2015.03.110
[16] Yin Z, Zhang J. Task-generic mental fatigue recognition based on neurophysiological signals and dynamical deep extreme learning machine. Neurocomputing 2018; 283: 266-281. doi: 10.1016/j.neucom.2017.12.062
[17] Tang J, Deng C, Huang GB. Extreme learning machine for multilayer perceptron. IEEE Transactions on Neural Networks and Learning Systems 2016; 27: 809-821. doi: 10.1109/TNNLS.2015.2424995
[18] Altan G, Kutlu Y. Hessenberg Elm Autoencoder Kernel for deep learning. Journal of Engineering Technology and Applied Sciences 2018; 3(2): 141-151. doi: 10.30931/jetas.450252
[19] Khatab ZE, Hajihoseini A, Ghorashi SA. A fingerprint method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sensors Letters 2018; 2: 1-4. doi: 10.1109/LSENS.2017.2787651
[20] Altan G, Kutlu Y, Garbi Y, Pekmezci AO, Nural S. Multimedia respiratory database (RespiratoryDatabase@TR): Auscultation sounds and chest x-rays. Natural and Engineering Sciences 2017; 2: 59-72. doi: 10.28978/ne- sciences.349282
[21] Vannuccini L, Earis JE, Helisto P, Cheetham BMG, Rossi M et al. Capturing and preprocessing of respiratory sounds. European Respiratory Review 2000; 10(77): 616-620.
[22] Koivunen AC, Kostinski AB. The feasibility of data whitening to improve performance of weather radar. Journal of Applied Meteorology 1999: 38(6): 741-749, doi: 10.1175/1520-0450(1999)038<0741:TFODWT>2.0.CO;2
[23] Pachori RB, Patidar S. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Computer Methods and Programs in Biomedicine 2014; 113(2): 494-502 doi: 10.1016/j.cmpb.2013.11.014
[24] Altan G, Kutlu Y, Yeniad M. ECG based human identification using second order difference plots. Computer Methods and Programs in Biomedicine 2019; 170: 81-93. doi: 10.1016/j.cmpb.2019.01.010
[25] Song G, Dai Q. A novel double deep ELMs ensemble system for time series forecasting. Knowledge-Based Systems 2017; 134: 31-49. doi: 10.1016/j.knosys.2017.07.014
26] Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing 2006; 70: 489-501. doi: 10.1016/j.neucom.2005.12.126
[27] Sevinc E, Dokeroglu T. A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines. Turkish Journal of Electrical Engineering & Computer Sciences 2019: 27(2): 1523-1533. doi: 10.3906/elk-1802-40
[28] Shebab MA, Kahraman N. Optimum, projected, and regularized extreme learning machine methods with singular value decomposition and L 2 -Tikhonov regularization. Turkish Journal of Electrical Engineering & Computer Sciences 2018; 26: 1685-1697. doi: 10.3906/elk-1706-60
[29] Altan G, Kutlu Y. Generative autoencoder kernels on deep learning for brain activity analysis. Natural and Engineering Sciences 2018: 3(3): 311-322. doi: 10.28978/nesciences.468978
[30] Golub GH, Van Loan CF. The Hessenberg and Real Schur Forms, 7.4. In: Golub GH, Loan CFV (editors). Matrix Computations, 3rd ed. Baltimore: Johns Hopkins University, 1996, pp. 1-20.
[31] Kutlu Y, Yayik A, Yildirim E, Yildirim S. LU triangularization extreme learning machine in EEG cognitive task classification. Neural Computing and Applications 2019; 31(4): 1117-1126. doi: 10.1007/s00521-017-3142-1
[32] Isler Y, Narin A, Ozer M, Perc M. Multi-stage classification of congestive heart failure based on short-term heart rate variability. Chaos, Solitons & Fractals 2019; 118: 145–151. doi: 10.1016/j.chaos.2018.11.020
[33] Moghadas-Dastjerdi H, Ahmadzadeh M, Karami E, Karami M, Samani A. Lung CT image based automatic technique for COPD GOLD stage assessment. Expert Systems with Applications 2017; 85: 194-203. doi: 10.1016/j.eswa.2017.05.036
[34] Isik U, Guven A, Buyukoglan H. Chronic obstructive pulmonary disease classification with artificial neural networks. In: 2015 Medical Technologies National Conference (TIPTEKNO); Muğla, Turkey; 2015. pp 1–4. (in Turkish with an abstract in English) doi:10.1109/TIPTEKNO.2015.7374589
[35] Ying J, Dutta J, Guo N, Xia L, Sitek A et al. Gold classification of COPDGene cohort based on deep learning. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Shanghai, China; 2016. pp. 2474-2478. doi: 10.1109/ICASSP.2016.7472122