Spectral pattern of cough sounds as an aid to diagnosis in children with common respiratory diseases
DOI:
https://doi.org/10.18203/2349-3291.ijcp20250770Keywords:
Spectral pattern, Random forest, Mel-frequency cepstral coefficientAbstract
Background: Objectives were to identify discriminatory spectral patterns of cough consistent with common respiratory sounds, through machine learning, to assist diagnosis.
Methods: Spectral analysis of cough recordings of 50 children each with crackles alone, wheezing alone and absence of crackles and wheezing. Identification of unique features through machine learning by dividing them into training (75%) and testing (25%) datasets. Feature extraction was done using R python and Librosa programming language. Two class classifications of the features extracted from the training dataset were done using classifier models like support vector machine, random forest, K nearest neighbor and classification and regression tree. Identification of the most suited classifier model that could accurately differentiate between the studied respiratory sounds in terms of sensitivity and specificity.
Results: Random forest classifier model using Mel-frequency cepstral coefficient (MFCC) gave the best results in differentiating crackles from wheezing with sensitivity and specificity of 66.67% and 66.67%. Classifier model performance improved when augmented with clinical features (Respiratory rate, history of recurrent nebulization and family history of atopy); providing sensitivity and specificity of 83.33% and 91.67%.
Conclusions: Cough features extracted and classified by machine learning can be used for non-auscultatory diagnosis of crackles and wheeze. This raises the possibility to develop smart applications for possible use by non-medical personnel to enhance their capability.
Metrics
References
Amrulloh Y, Abeyratne U, Swarnkar V, Triasih R. Cough sound analysis for pneumonia and asthma classification in pediatric population. In 2015 6th International Conference on Intelligent Systems, Modelling and Simulation. 2015;127-31. DOI: https://doi.org/10.1109/ISMS.2015.41
Chowdhury SK, Majumder AK. Digital Spectrum Analysis of Respiratory Sound. IEEE Transactions on Biomedical Engineering. Institute of Electrical and Electronics Engineers (IEEE); 1981;BME28(11):784-8. DOI: https://doi.org/10.1109/TBME.1981.324768
Korpas J, Korpasova-Sadlonova J. Cough sound registration in man. Folia Med Martiniana. 1984;10:167-93
Debreczeni LA, Korpas J, Salat D. Spectral analysis of cough sounds recorded with and without a nose clip. Bull Eur Physiopathol Respirat. 1987;23:57s-61s.
Korpáš J, Sadloňová J, Vrabec M. Analysis of the cough sound: an overview. Pulmonary Pharmacol. 1996;9(5-6):261-8. DOI: https://doi.org/10.1006/pulp.1996.0034
Abeyratne UR, Swarnkar V, Setyati A, Triasih R. Cough sound analysis can rapidly diagnose childhood pneumonia. Ann Biomed Engineering. 2013;41(11):2448-62. DOI: https://doi.org/10.1007/s10439-013-0836-0
Toop LL, Dawson KK, Thorpe CW. A portable system for the spectral analysis of cough sounds in asthma. J Asthma. 1990;27(6):393-7. DOI: https://doi.org/10.3109/02770909009073356
Sharan RV, Abeyratne UR, Swarnkar VR, Porter P. Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2017;4578-81. DOI: https://doi.org/10.1109/EMBC.2017.8037875
Amrulloh YA, Abeyratne UR, Swarnkar V, Herath D, Triasih R, Setyati A. HMM Based Cough Sound Analysis for Classifying Pneumonia and Asthma in Pediatric Population. IFMBE Proceedings. Springer Singapore. 2017;852 DOI: https://doi.org/10.1007/978-981-10-5122-7_213
Amrulloh YA, Abeyratne UR, Swarnkar V, Triasih R, Setyati A. Automatic cough segmentation from non-contact sound recordings in pediatric wards. Biomedical Signal Processing and Control. Elsevier BV. 2015;21:126-36. DOI: https://doi.org/10.1016/j.bspc.2015.05.001
Dawson KP, Thorpe CW, Toop LJ. The spectral analysis of cough sounds in childhood respiratory illness. J Paediatr Child Heal. 1991;27(1):4-6. DOI: https://doi.org/10.1111/j.1440-1754.1991.tb00336.x
Kosasih K, Abeyratne UR, Swarnkar V, Triasih R. Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis. IEEE Transactions on Biomed Engineering. 2014;62(4):1185-94. DOI: https://doi.org/10.1109/TBME.2014.2381214
Kruizinga MD, Zhuparris A, Dessing E, Krol FJ, Sprij AJ, Doll RJ, et al. Development and technical validation of a smartphone‐based pediatric cough detection algorithm. Pediat Pulmonol. 2022;57(3):761-7. DOI: https://doi.org/10.1002/ppul.25801
Venditto L, Morano S, Piazza M, Zaffanello M, Tenero L, Piacentini G, et al. Artificial intelligence and wheezing in children: where are we now? Front Med. 2024;11:1460050. DOI: https://doi.org/10.3389/fmed.2024.1460050
Downloads
Published
How to Cite
Issue
Section
