Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

Sangeetha Balasubramanian; Periyasamy Rajadurai

doi:10.46604/ijeti.2023.12294

Authors

Sangeetha Balasubramanian Department of Instrumentation and Control Engineering, National Institute of Technology, Tamil Nādu, India
Periyasamy Rajadurai Department of Instrumentation and Control Engineering, National Institute of Technology, Tamil Nādu, India

DOI:

https://doi.org/10.46604/ijeti.2023.12294

Keywords:

cepstral coefficients, discrete wavelet transform, machine learning classifiers, pulmonary diseases, variational mode decomposition

Abstract

The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings.

References

S. M. Levine and D. D. Marciniuk, “Global Impact of Respiratory Disease: What Can We Do, Together, to Make a Difference?” Chest, vol. 161, no. 5, pp. 1153-1154, May 2022.

B. Abera Tessema, H. D. Nemomssa, and G. Lamesgin Simegn, “Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases,” Medical Devices: Evidence and Research, vol. 15, pp. 89-102, 2022.

T. Grzywalski, M. Piecuch, M. Szajek, A. Bręborowicz, H. Hafke-Dys, J. Kociński, et al., “Practical Implementation of Artificial Intelligence Algorithms in Pulmonary Auscultation Examination,” European Journal of Pediatrics, vol. 178, no. 6, pp. 883-890, June 2019.

B. M. Rocha, D. Filos, L. Mendes, G. Serbes, S. Ulukaya, Y. P. Kahya, et al., “An Open Access Database for the Evaluation of Respiratory Sound Classification Algorithms,” Physiological Measurement, vol. 40, no. 3, article no. 035001, March 2019.

Y. Feng, Y. Wang, C. Zeng, and H. Mao, “Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease,” International Journal of Medical Sciences, vol. 18, no. 13, pp. 2871-2889, 2021.

Y. Shi, Y. Li, M. Cai, and X. D. Zhang, “A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network,” International Journal of Biological Sciences, vol. 15, no. 1, pp. 195-207, 2019.

G. Petmezas, G. A. Cheimariotis, L. Stefanopoulos, B. Rocha, R. P. Paiva, A. K. Katsaggelos, et al., “Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function,” Sensors, vol. 22, no. 3, February 2022.

J. Acharya and A. Basu, “Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning,” IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 3, pp. 535-544, June 2020.

S. B. Shuvo, S. N. Ali, S. I. Swapnil, T. Hasan, and M. I. H. Bhuiyan, “A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp. 2595-2603, July 2021.

B. Acar Demirci, Y. Koçyiğit, D. Kizilirmak, and Y. Havlucu, “Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods,” Celal Bayar University Journal of Science, vol. 18, no. 2, pp. 169-180, 2022.

A. Gökçen, “Computer-Aided Diagnosis System for Chronic Obstructive Pulmonary Disease Using Empirical Wavelet Transform on Auscultation Sounds,” The Computer Journal, vol. 64, no. 11, pp. 1775-1783, November 2021.

N. S. Haider and A. K. Behera, “Computerized Lung Sound Based Classification of Asthma and Chronic Obstructive Pulmonary Disease (COPD),” Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 42-59, January-March 2022.

N. Asatani, T. Kamiya, S. Mabu, and S. Kido, “Classification of Respiratory Sounds Using Improved Convolutional Recurrent Neural Network,” Computers & Electrical Engineering,” vol. 94, article no. 107367, September 2021.

A. Rizal and A. Puspitasari, “Lung Sound Classification Using Wavelet Transform and Entropy to Detect Lung Abnormality,” Serbian Journal of Electrical Engineering, vol. 19, no. 1, pp. 79-98, 2022.

S. Gupta, M. Agrawal, and D. Deepak, “Gammatonegram Based Triple Classification of Lung Sounds Using Deep Convolutional Neural Network with Transfer Learning,” Biomedical Signal Processing and Control, vol. 70, article no. 102947, September 2021.

M. Mathe, P. Mididoddi, and B. T. Krishna, “Artifact Removal Methods in EEG Recordings: A Review,” Proceedings Engineering and Technology Innovation, vol. 20, pp. 35-56, January 2022.

G. Sharma, K. Umapathy, and S. Krishnan, “Trends in Audio Signal Feature Extraction Methods,” Applied Acoustics, vol. 158, article no. 107020, January 2020.

M. M. Zarandah, S. M. Daud, and S. S. Abu-Naser, “A Systematic Literature Review of Machine and Deep Learning-Based Detection and Classification Methods for Diseases Related to the Respiratory System,” Journal of Theoretical and Applied Information Technology, vol. 101, no. 4, pp. 1273-1296, February 2023.

T. A. Assegie and Y. B. Chekol, “The Performance of Machine Learning for Chronic Kidney Disease Diagnosis,” Emerging Science Innovation, vol. 1, pp. 33-40, September 2023.

A. Hussain, H. Malik, and M. U. Chaudhry, “Supervised Learning Based Classification of Cardiovascular Diseases,” Proceedings of Engineering and Technology Innovation, vol. 20, pp. 24-34, January 2022.

A. Elsetrønning, A. Rasheed, J. Bekker, and O. San, “On the Effectiveness of Signal Decomposition, Feature Extraction and Selection on Lung Sound Classification,” https://doi.org/10.48550/arXiv.2012.11759, December 22, 2020.

S. Y. Jung, C. H. Liao, Y. S. Wu, S. M. Yuan, and C. T. Sun, “Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features,” Diagnostics, vol. 11, no. 4, article no. 732, April 2021.

S. Rani, A. Chaurasia, M. K. Dutta, V. Myska, and R. Burget, “Machine Learning Approach for Automatic Lungs Sound Diagnosis from Pulmonary Signals,” 44th International Conference on Telecommunications and Signal Processing (TSP), pp. 366-371, July 2021.

L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, “A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection,” Applied Sciences, vol. 12, no. 8, article no. 3877, April 2022.

Z. Neili and K. Sundaraj, “Gammatonegram Based Pulmonary Pathologies Classification Using Convolutional Neural Networks,” 19th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 1112-1118, May 2022.

S. A. F. Jaffery, S. Aziz, M. U. Khan, S. Z. H. Naqvi, M. Faraz, and A. Usman, “An Automated System for the Classification of Bronchiolitis and Bronchiectasis Diseases Using Lung Sound Analysis,” International Conference on Robotics and Automation in Industry, pp. 1-6, March 2023.