Volume 17, Issue 2 (9-2020)
JSDP 2020, 17(2): 70-59 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Karimizadeh A, Vali M, modaresi M. Symmetry of Frequency information in Right and Left Lung sound and Infection Detection in Cystic Fibrosis Patients. JSDP 2020; 17 (2) :70-59
URL: http://jsdp.rcisp.ac.ir/article-1-894-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-894-en.html
K. N. Toosi University of Technology
Abstract: (2930 Views)
Cystic fibrosis (CF) is the most common autosomal recessive disorder in white skinned individuals. Chronic lung infection is the main cause of mortality in this disease. Approximately 60–75 % of adult CF patients frequently suffer from Pseudomonas aeruginosa (PA) infection that is strongly associated with inflammation, lung destruction, and increased mortality. Therefore, CF patients should be followed up by physicians to diagnose infection in the primary stage, start treatment, and reduce the risk of chronic infection. Although sputum culture is the gold standard for diagnosis of PA infections, a rapid and accurate diagnostic method can facilitate early initiation of appropriate therapy and easy monitoring of the condition. The aim of this study was to diagnose CF patients with infection using their lung sound.
In this study, the symmetry of frequency information in right and left lung was investigated in CF patients with positive sputum culture results, negative sputum culture results, and patients who underwent treatment with antibiotics. Respiretorysounds were acquired from 34 CF patients (16 female, 18 male) who were being followed-up at the Pediatric Respiratory and Sleep Medicine Research Center of Children's Medical Center. The patient selection was based on their sputum microbiology culture. The selection category was as follows: 12 patients with normal flora culture results and 11 patients with PA infection. Also, respiratory sounds of 11 patients were recorded one month after antibiotic treatment and they used to investigate the effectiveness of the proposed method.
In the preprocessing step, cardiac sound was removed, respiratory sound cycles were separated and the signals were divided into 64 milisecond frame and 15 features were extracted from each frame. Differences between these features were computed between right and left lungs for early, middle and late section of the respiratory cycle using the new proposed feature. Then, the best group of features was selected by applying Genetic Algorithm. The selected group of features was fed into Support Vector Machine, K Nearest Neighbor and Naïve Bayesian classifier. Also, an Ensemble classifier was examined. The best result was obtained by Ensemble classifier that diagnosed infection by the accuracy of 91.3% and differentiates a group of CF patients with infection from CF patients who underwent treatment with an accuracy of 90.9%. This study describes a novel method of infection detection in CF patients based only on respiratory sound analysis. The proposed method is a simple and available way for early diagnosis of infection and initiating therapeutic strategies.
In this study, the symmetry of frequency information in right and left lung was investigated in CF patients with positive sputum culture results, negative sputum culture results, and patients who underwent treatment with antibiotics. Respiretorysounds were acquired from 34 CF patients (16 female, 18 male) who were being followed-up at the Pediatric Respiratory and Sleep Medicine Research Center of Children's Medical Center. The patient selection was based on their sputum microbiology culture. The selection category was as follows: 12 patients with normal flora culture results and 11 patients with PA infection. Also, respiratory sounds of 11 patients were recorded one month after antibiotic treatment and they used to investigate the effectiveness of the proposed method.
In the preprocessing step, cardiac sound was removed, respiratory sound cycles were separated and the signals were divided into 64 milisecond frame and 15 features were extracted from each frame. Differences between these features were computed between right and left lungs for early, middle and late section of the respiratory cycle using the new proposed feature. Then, the best group of features was selected by applying Genetic Algorithm. The selected group of features was fed into Support Vector Machine, K Nearest Neighbor and Naïve Bayesian classifier. Also, an Ensemble classifier was examined. The best result was obtained by Ensemble classifier that diagnosed infection by the accuracy of 91.3% and differentiates a group of CF patients with infection from CF patients who underwent treatment with an accuracy of 90.9%. This study describes a novel method of infection detection in CF patients based only on respiratory sound analysis. The proposed method is a simple and available way for early diagnosis of infection and initiating therapeutic strategies.
Type of Study: Research |
Subject:
Paper
Received: 2018/09/2 | Accepted: 2019/05/7 | Published: 2020/09/14 | ePublished: 2020/09/14
Received: 2018/09/2 | Accepted: 2019/05/7 | Published: 2020/09/14 | ePublished: 2020/09/14
References
1. [1] M. A. Koda-Kimble, Koda-Kimble and Young's applied therapeutics: the clinical use of drugs: Lippincott Williams & Wilkins, 2012.
2. [2] M. modaresi, J. faghihinia, and F baharzadeh, "Cystic Fibrosis Prevalence among a Group of High-Risk Iranian Children ," Journal of Isfahan Medical School, vol. 30, 2012.
3. [3] N. Pillarisetti, E. Williamson, B. Linnane, B. Skoric, C. F. Robertson, P. Robinson, J. Massie, G. L. Hall, P. Sly, and S. Stick, "Infection, inflammation, and lung function decline in infants with cystic fibrosis," American journal of respiratory and critical care medicine, vol. 184, pp. 75-81, 2011. [DOI:10.1164/rccm.201011-1892OC] [PMID]
4. [4] H. G. Ahlgren, A. Benedetti, J. S. Landry, J. Bernier, E. Matouk, D. Radzioch, L. C. Lands, S. Rousseau, and D. Nguyen, "Clinical outcomes associated with Staphylococcus aureus and Pseudomonas aeruginosa airway infections in adult cystic fibrosis patients," BMC pulmonary medicine, vol. 15, pp. 67, 2015. [DOI:10.1186/s12890-015-0062-7] [PMID] [PMCID]
5. [5] Z. Li, M. R. Kosorok, P. M. Farrell, A. Laxova, S. E. West, C. G. Green, J. Collins, M. J. Rock, and M. L. Splaingard, "Longitudinal development of mucoid Pseudomonas aeruginosa infection and lung disease progression in children with cystic fibrosis," Jama, vol. 293, pp. 581-588, 2005. [DOI:10.1001/jama.293.5.581] [PMID]
6. [6] K. M. Langan, T. Kotsimbos, and A. Y. Peleg, "Managing Pseudomonas aeruginosa respiratory infections in cystic fibrosis," Current opinion in infectious diseases, vol. 28, pp. 547-556, 2015. [DOI:10.1097/QCO.0000000000000217] [PMID]
7. [7] P. J. Mogayzel Jr, E. T. Naureckas, K. A. Robinson, C. Brady, M. Guill, T. Lahiri, L. Lubsch, J. Matsui, C. M. Oermann, and F. Ratjen, "Cystic Fibrosis Foundation pulmonary guideline. Pharmacologic approaches to prevention and eradication of initial Pseudomonas aeruginosa infection," Annals of the American Thoracic Society, vol. 11, pp. 1640-1650, 2014. [DOI:10.1513/AnnalsATS.201404-166OC] [PMID]
8. [8] A. R. Smyth, S. C. Bell, S. Bojcin, M. Bryon, A. Duff, P. Flume, N. Kashirskaya, A. Munck, F. Ratjen, and S. J. Schwarzenberg, "European cystic fibrosis society standards of care: best practice guidelines," Journal of cystic fibrosis, vol. 13, pp. S23-S42, 2014. [DOI:10.1016/j.jcf.2014.03.010] [PMID]
9. [9] S. Ferrari, M. Silva, M. Guarino, J. M. Aerts, and D. Berckmans, "Cough sound analysis to identify respiratory infection in pigs," Computers and Electronics in Agriculture, vol. 64, pp. 318-325, 2008. [DOI:10.1016/j.compag.2008.07.003]
10. [10] A. Oliveira, C. Pinho, J. Dinis, D. Oliveira, and A. Marques, "Automatic Wheeze Detection and Lung Function Evaluation-A Preliminary Study," in HEALTHINF, 2013, pp. 323-326.
11. [11] J. Niu, Y. Shi, M. Cai, Z. Cao, D. Wang, Z. Zhang, and X. D. Zhang, "Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques," Bioinformatics, vol. 34, pp. 820-827, 2017. [DOI:10.1093/bioinformatics/btx652] [PMID] [PMCID]
12. [12] J. Niu, Y. Shi, D. Shen, Y. Wang, W. Xu, M. Cai, and Y. Li, "The Identification of Sputum Situation Based on the Sound from the Respiratory Tract," in 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2018, pp. 1166-1171. [DOI:10.1109/AIM.2018.8452413] [PMID] [PMCID]
13. [13] Y. Shi, G. Wang, J. Niu, Q. Zhang, M. Cai, B. Sun, D. Wang, M. Xue, and X. D. Zhang, "Classification of sputum sounds using artificial neural network and wavelet transform," Int. J. Biol. Sci, 2018. [DOI:10.7150/ijbs.23855] [PMID] [PMCID]
14. [14] W. L. Wilkins, "Auscultation skills: breath and heart sounds," Auscultation Skills: Breath and Heart Sounds, pp. 156-157, 2009.
15. [15] R. Dosani and S. Kraman, "Lung sound intensity variability in normal men: a contour phonopneumographic study," Chest, vol. 83, pp. 628-631, 1983. [DOI:10.1378/chest.83.4.628] [PMID]
16. [16] H. Pasterkamp, S. Patel, and G. Wodicka, "Asymmetry of respiratory sounds and thoracic transmission," Medical and Biological Engi-neering and Computing, vol. 35, pp. 103-106, 1997. [DOI:10.1007/BF02534138] [PMID]
17. [17] Z. K. Moussavi, M. T. Leopando, H. Pasterkamp, and G. Rempel, "Computerised acoustical respiratory phase detection without airflow measurement," Medical and Biological Engineering and Computing, vol. 38, pp. 198-203, 2000. [DOI:10.1007/BF02344776] [PMID]
18. [18] R. P. Dellinger, J. E. Parrillo, A. Kushnir, M. Rossi, and I. Kushnir, "Dynamic visualization of lung sounds with a vibration response device: a case series," Respiration, vol. 75, pp. 60-72, 2008. [DOI:10.1159/000103558] [PMID]
19. [19] A. Torres-Jimenez, S. Charleston-Villalobos, R. Gonzalez-Camarena, G. Chi-Lem, and T. Aljama-Corrales, "Asymmetry in lung sound intensities detected by respiratory acoustic thoracic imaging (RATHI) and clinical pulmonary auscultation," in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, 2008, pp. 4797-4800. [DOI:10.1109/IEMBS.2008.4650286] [PMID]
20. [20] J. Gnitecki and Z. M. Moussavi, "Separating heart sounds from lung sounds," IEEE Engineering in medicine and biology magazine, vol. 26, pp. 20, 2007. [DOI:10.1109/MEMB.2007.289118] [PMID]
21. [21] D. S. Morillo, S. A. Moreno, M. Á. F. Granero, and A. L. Jiménez, "Computerized analysis of respiratory sounds during COPD exacerbations," Computers in biology and medicine, vol. 43, pp. 914-921, 2013. [DOI:10.1016/j.compbiomed.2013.03.011] [PMID]
22. [22] M. Tenhunen, E. Rauhala, E. Huupponen, A. Saastamoinen, A. Kulkas, and S. Himanen, "High frequency components of tracheal sound are emphasized during prolonged flow limitation," Physiological measurement, vol. 30, pp. 467, 2009. [DOI:10.1088/0967-3334/30/5/004] [PMID]
23. [23] S. Charleston-Villalobos, L. Albuerne-Sanchez, R. Gonzalez-Camarena, M. Mejia-Avila, G. Carrillo-Rodriguez, and T. Aljama-Corrales, "Linear and nonlinear analysis of base lung sound in extrinsic allergic alveolitis patients in comparison to healthy subjects," Methods of information in medicine, vol. 52, pp. 266-276, 2013. [DOI:10.3414/ME12-01-0037] [PMID]
24. [24] V. Rocha, C. Melo, and A. Marques, "Computerized respiratory sound analysis in people with dementia: a first-step towards diagnosis and monitoring of respiratory conditions," Physiological measurement, vol. 37, pp. 2079, 2016. [DOI:10.1088/0967-3334/37/11/2079] [PMID]
25. [25] R. Naves, B. H. Barbosa, and D. D. Ferreira, "Classification of lung sounds using higher-order statistics: A divide-and-conquer approach," Computer methods and programs in biomedicine, vol. 129, pp. 12-20, 2016. [DOI:10.1016/j.cmpb.2016.02.013] [PMID]
26. [26] O. Kramer, Genetic algorithm essentials vol. 679: Springer, 2017. [DOI:10.1007/978-3-319-52156-5]
27. [27] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, and S. Y. Philip, "Top 10 algorithms in data mining," Knowledge and information systems, vol. 14, pp. 1-37, 2008. [DOI:10.1007/s10115-007-0114-2]
28. [28] M. Rahbaripour and B. M. Asl, "Premature Ventricular Contraction Arrythmia Detection in ECG Signals via Combined Classifiers," Signal and Data Processing, vol. 1, 2018. [DOI:10.29252/jsdp.15.1.55]
29. [29] L. I. Kuncheva, J. C. Bezdek, and R. P. Duin, "Decision templates for multiple classifier fusion: an experimental comparison," Pattern recognition, vol. 34, pp. 299-314, 2001. [DOI:10.1016/S0031-3203(99)00223-X]
30. [30] A. Bohadana, G. Izbicki, and S. S. Kraman, "Fundamentals of lung auscultation," New England Journal of Medicine, vol. 370, pp. 744-751, 2014. [DOI:10.1056/NEJMra1302901] [PMID]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
