VARYASYONEL MOD AYRIŞTIRMASIYLA ÖKSÜRÜK SESLERİNDEN KOVİD-19 TESPİTİ

Year 2023, Volume: 11 Issue: 2, 354 - 369, 01.06.2023

Fatma Zehra Solak

https://doi.org/10.36306/konjes.1110235

Abstract

Dünya Sağlık Örgütü'ne göre öksürük, küresel bir pandemi olarak ilan edilen COVID-19 hastalığının en belirgin semptomlarından biridir. Bu semptom, tıbbi muayene için kliniğe gelen COVID-19 hastası kişilerin %68 ila %83'ünde görülür. Bu nedenle pandemi sırasında, öksürük COVID-19'un teşhis edilmesinde ve hastaların sağlıklılardan ayırt edilmesinde önemli bir rol oynamaktadır. Bu çalışma, COVID-19 pozitif kişilerin öksürük seslerini COVID-19 negatif olanlardan ayırt etmeyi ve böylece COVID-19 tanısına destek sağlamayı amaçlamaktadır. Bu amaçla COVID-19 ve COVID-19 değil olarak etiketlenmiş öksürük seslerini içeren “Virufy” veri seti dahil edilmiştir. Verileri dengelemek için ADASYN tekniği kullanıldıktan sonra, Varyasyonel Mod Ayrıştırma (VMD) yöntemi kullanılarak her bir ses için bağımsız modlar elde edilmiş ve her moddan çeşitli öznitelikler çıkarılmıştır. Daha sonra, ReliefF algoritması ile en etkili olan özellikler seçilmiştir. Ardından, sınıflandırma yoluyla COVID-19 ve COVID-19 olmayan öksürük seslerini tanımlamak için topluluk makine öğrenme yöntemleri (Rastgele Orman, Gradyan Artırma Makineleri ve Adaboost) tercih edilmiştir. Sonuç olarak en iyi performans Gradyan Artırma Makineleri ile %94,19 doğruluk, %87,67 duyarlılık, %100 özgüllük, %100 kesinlik, %93,43 F-skor, 0,88 kappa ve %93,87 ROC eğrisi altında kalan alan olarak elde edilmiştir.

Keywords

ADASYN, Öksürük Sesleri, COVID-19, Topluluk Makine Öğrenmesi, ReliefF, Varyasyonel Mod Ayrıştırma (VMD)

COVID-19 DETECTION USING VARIATIONAL MODE DECOMPOSITION OF COUGH SOUNDS

Abstract

According to the World Health Organization, cough is one of the most prominent symptoms of the COVID-19 disease declared as a global pandemic. The symptom is seen in 68% to 83% of people with COVID-19 who come to the clinic for medical examination. Therefore, during the pandemic, cough plays an important role in diagnosing of COVID-19 and distinguishing patients from healthy individuals. This study aims to distinguish the cough sounds of COVID-19 positive people from those of COVID-19 negative, thus providing automatic detection and support for the diagnosis of COVID-19. For this aim, “Virufy” dataset containing cough sounds labeled as COVID-19 and Non COVID-19 was included. After using the ADASYN technique to balance the data, independent modes were obtained for each sound by utilizing the Variational Mode Decomposition (VMD) method and various features were extracted from every mode. Afterward, the most effective features were selected by ReliefF algorithm. Following, ensemble machine learning methods, namely Random Forest, Gradient Boosting Machine and Adaboost were prepared to identify cough sounds as COVID-19 and Non COVID-19 through classification. As a result, the best performance was obtained with the Gradient Boosting Machine as 94.19% accuracy, 87.67% sensitivity, 100% specificity, 100% precision, 93.43% F-score, 0.88 kappa and 93.87% area under the ROC curve.

Keywords

ADASYN, Cough Sound, COVID-19, Ensemble Machine Learning, ReliefF, Variational Mode Decomposition (VMD)

References

• [1] A. Narin, C. Kaya, and Z. Pamuk, "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks," Pattern Anal Appl, pp. 1-14, May 9 2021.
• [2] M. Pahar, M. Klopper, R. Warren, and T. Niesler, "COVID-19 cough classification using machine learning and global smartphone recordings," Comput Biol Med, vol. 135, p. 104572, Aug 2021.
• [3] P. Mouawad, T. Dubnov, and S. Dubnov, "Robust Detection of COVID-19 in Cough Sounds: Using Recurrence Dynamics and Variable Markov Model," SN Comput Sci, vol. 2, no. 1, p. 34, 2021.
• [4] A. Pal and M. Sankarasubbu, "Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing," in 36th ACM/SIGAPP Symposium on Applied Computing (SAC ’21), March 22–26, 2021, pp. 620-628.
• [5] P. Bagad et al., "Cough Against: COVID Evidence of COVID-19 Signature in Cough Sounds," Preprint from arXiv, 2020.
• [6] G. Chaudhari et al., "Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough," ArXiv, vol. 2011.13320, 2020.
• [7] A. Imran et al., "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app," Inform Med Unlocked, vol. 20, p. 100378, 2020.
• [8] A. Fakhry, X. Jiang, J. Xiao, G. Chaudhari, A. Han, and A. Khanzada, "Virufy: A Multi-Branch Deep Learning Network for Automated Detection of Covid-19," preprint from arXiv:2103.01806, 2021.
• [9] N. Melek Manshouri, "Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study," Cogn Neurodyn, pp. 1-15, Jul 29 2021.
• [10] M. R. Kamble et al., "PANACEA cough sound-based diagnosis of COVID-19 for the DiCOVA 2021 Challenge," arXiv preprint arXiv:2106.04423, 2021.
• [11] S. Rao, V. Narayanaswamy, M. Esposito, J. Thiagarajan, and A. Spanias, "Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection," in 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), 2021, pp. 1-5: IEEE.
• [12] Y. E. Erdoğan and A. Narin, "COVID-19 detection with traditional and deep features on cough acoustic signals," Computers in Biology and Medicine, vol. 136, p. 104765, 2021.
• [13] A. Tena, F. Clarià, and F. Solsona, "Automated detection of COVID-19 cough," Biomedical Signal Processing and Control, vol. 71, p. 103175, 2022.
• [14] R. Islam, E. Abdel-Raheem, and M. Tarique, "A study of using cough sounds and deep neural networks for the early detection of COVID-19," Biomedical Engineering Advances, vol. 3, p. 100025, 2022.
• [15] M. Aly, K. H. Rahouma, and S. M. Ramzy, "Pay attention to the speech: COVID-19 diagnosis using machine learning and crowdsourced respiratory and speech recordings," Alexandria Engineering Journal, 2021.
• [16] H. Coppock, A. Gaskell, P. Tzirakis, A. Baird, L. Jones, and B. W. Schuller, "End-2-End COVID-19 Detection from Breath & Cough Audio," Preprint from arXiv:2102.08359v1, 2021.
• [17] L. Khriji, A. Ammari, S. Messaoud, S. Bouaafia, A. Maraoui, and M. Machhout, "COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis: Deep Learning Approach," in 2021 29th Conference of Open Innovations Association (FRUCT), 2021, pp. 185-191: IEEE.
• [18] D. Grant, I. McLane, and J. West, "Rapid and scalable COVID-19 screening using speech, breath, and cough recordings," in 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2021, pp. 1-6: IEEE.
• [19] K. K. Lella and A. Pja, "Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath," Alexandria Engineering Journal, vol. 61, no. 2, pp. 1319-1334, 2022.
• [20] K. Dragomiretskiy and D. Zosso, "Variational mode decomposition," IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, 2014.
• [21] S. Deb, S. Dandapat, and J. Krajewski, "Analysis and classification of cold speech using variational mode decomposition," IEEE Transactions on Affective Computing, vol. 11, no. 2, pp. 296-307, 2017.
• [22] I. Kononenko, "Estimating Attributes: Analysis and Extensions of Relief," presented at the European Conference on Machine Learning, 1994.
• [23] J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, "COVID-19 open source data sets: a comprehensive survey," Applied Intelligence, vol. 51, no. 3, pp. 1296-1325, 2021.
• [24] H. He, Y. Bai, E. A. Garcia, and S. Li, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning," presented at the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008.
• [25] F. H. K. d. S. Tanaka and C. Aranha, "Data augmentation using GANs," arXiv preprint arXiv:1904.09135, 2019.
• [26] H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on knowledge and data engineering, vol. 21, no. 9, pp. 1263-1284, 2009.
• [27] J. Zhu, P. Wu, H. Chen, J. Liu, and L. Zhou, "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and Its Applications, vol. 519, pp. 140-158, 2019.
• [28] B. Karan, K. Mahto, and S. S. Sahu, "Detection of Parkinson disease using variational mode decomposition of speech signal," in 2018 International Conference on Communication and Signal Processing (ICCSP), 2018, pp. 0508-0512: IEEE.
• [29] H. Yang, Y. Cheng, and G. Li, "A denoising method for ship radiated noise based on Spearman variational mode decomposition, spatial-dependence recurrence sample entropy, improved wavelet threshold denoising, and Savitzky-Golay filter," Alexandria Engineering Journal, vol. 60, no. 3, pp. 3379-3400, 2021.
• [30] J. M. Yentes, N. Hunt, K. K. Schmid, J. P. Kaipust, D. McGrath, and N. Stergiou, "The appropriate use of approximate entropy and sample entropy with short data sets," Annals of biomedical engineering, vol. 41, no. 2, pp. 349-365, 2013.
• [31] C. Bandt and B. Pompe, "Permutation entropy: a natural complexity measure for time series," Physical review letters, vol. 88, no. 17, p. 174102, 2002.
• [32] R. Sharma, R. B. Pachori, and U. R. Acharya, "Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals," Entropy, vol. 17, no. 2, pp. 669-691, 2014.
• [33] A. Stief, J. R. Ottewill, and J. Baranowski, "Relief F-based feature ranking and feature selection for monitoring induction motors," in 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR), 2018, pp. 171-176: IEEE.
• [34] S. Mao, J.-W. Chen, L. Jiao, S. Gou, and R. Wang, "Maximizing diversity by transformed ensemble learning," Applied Soft Computing, vol. 82, p. 105580, 2019.
• [35] H. Liu and L. Zhang, "Advancing Ensemble Learning Performance through data transformation and classifiers fusion in granular computing context," Expert Systems with Applications, vol. 131, pp. 20-29, 2019.
• [36] T. K. Ho, "Random decision forests," in Proceedings of 3rd international conference on document analysis and recognition, 1995, vol. 1, pp. 278-282: IEEE.
• [37] T. Pinto, I. Praça, Z. Vale, and J. Silva, "Ensemble learning for electricity consumption forecasting in office buildings," Neurocomputing, vol. 423, pp. 747-755, 2021.
• [38] Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of computer and system sciences, vol. 55, no. 1, pp. 119-139, 1997.
• [39] O. Sagi and L. Rokach, "Ensemble learning: A survey," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1249, 2018.
• [40] V. A. Dev and M. R. Eden, "Formation lithology classification using scalable gradient boosted decision trees," Computers & chemical engineering, vol. 128, pp. 392-404, 2019.
• [41] V. Raj, A. Renjini, M. Swapna, S. Sreejyothi, and S. Sankararaman, "Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation," Chaos, Solitons & Fractals, vol. 140, p. 110246, 2020.
• [42] T. Tuncer, E. Akbal, E. Aydemir, S. B. Belhaouari, and S. Dogan, "A novel local feature generation technique based sound classification method for covid-19 detection using lung breathing sound," European Journal of Technique (EJT), vol. 11, no. 2, pp. 165-174, 2021.
• [43] T. TUNCER, E. Aydemir, F. ÖZYURT, S. Dogan, S. B. Belhaouari, and E. Akbal, "An automated Covid-19 respiratory sound classification method based on novel local symmetric Euclidean distance pattern and ReliefF iterative MRMR feature selector," International Advanced Researches and Engineering Journal, vol. 5, no. 3, pp. 334-343, 2021.