Cough Sound Analysis

Open access

Abstract

Cough is the most common symptom of many respiratory diseases. Currently, no standardized methods exist for objective monitoring of cough, which could be commercially available and clinically acceptable. Our aim is to develop an algorithm which will be capable, according to the sound events analysis, to perform objective ambulatory and automated monitoring of frequency of cough. Because speech is the most common sound in 24-hour recordings, the first step for developing this algorithm is to distinguish between cough sound and speech. For this purpose we obtained recordings from 20 healthy volunteers. All subjects performed continuous reading of the text from the book with voluntary coughs at the indicated instants. The obtained sounds were analyzed using by linear and non-linear analysis in the time and frequency domain. We used the classification tree for the distinction between cough sound and speech. The median sensitivity was 100% and the median specificity was 95%. In the next step we enlarged the analyzed sound events. Apart from cough sounds and speech the analyzed sounds were induced sneezing, voluntary throat and nasopharynx clearing, voluntary forced ventilation, laughing, voluntary snoring, eructation, nasal blowing and loud swallowing. The sound events were obtained from 32 healthy volunteers and for their analysis and classification we used the same algorithm as in previous study. The median sensitivity was 86% and median specificity was 91%. In the final step, we tested the effectiveness of our developed algorithm for distinction between cough and non-cough sounds produced during normal daily activities in patients suffering from respiratory diseases. Our study group consisted from 9 patients suffering from respiratory diseases. The recording time was 5 hours. The number of coughs counted by our algorithm was compared with manual cough counts done by two skilled co-workers. We have found that the number of cough analyzed by our algorithm and manual counting, as well, were disproportionately different. For that reason we have used another methods for the distinction of cough sound from non-cough sounds. We have compared the classification tree and artificial neural networks. Median sensitivity was increasing from 28% (classification tree) to 82% (artificial neural network), while the median specificity was not changed significantly. We have enlarged our characteristic parameters of the Mel frequency cepstral coefficients, the weighted Euclidean distance and the first and second derivative in time. Likewise the modification of classification algorithm is under our interest

References
  • 1. Woolf CR, Rosenberg A. Objective assessment of cough suppressants under clinical conditions using a tape recorder system. Thorax 1964; 19: 125-30.

  • 2. Loudon RG, Brown LC. Cough frequency in patients with respiratory disease. Am Rev Respir Dis 1967; 96(6): 1137-43.

  • 3. Barry SJ, Dane AD, Morice AH, Walmsley AD. The automatic recognition and counting of cough. Cough 2006; 2: 8, doi 10.1186/1745-9974-2-8.

  • 4. Matos S, Birring SS, Pavord ID, Evans DH. Detection of cough signals in continuous audio recordings using hidden Markov models. IEEE Trans Biomed Eng 2006; 53(6): 1078-83.

  • 5. Hsu JY, Stone RA, Logan-Sinclair RB, Worsdell M, Busst CM, Chung KF. Coughing frequency in patients with persistent cough: assessment using a 24 hour ambulatory recorder. Eur Respir J 1994; 7(7): 1246-53.

  • 6. Chang AB, Newman RG, Phelan PD, Robertson CF. A new use for an old Holter monitor: an ambulatory cough meter. Eur Respir J 1997; 10(7): 1637-9.

  • 7. Murata A, Taniguchi Y, Hashimoto Y, Kaneko Y, Takasaki Y, Kudoh S. Discrimination of productive and nonproductive cough by sound analysis. Intern Med 1998; 37(9): 732-5.

  • 8. Olia PM, Sestini P, Vagliasindi M. Acoustic parameters of voluntary cough in healthy non-smoking subjects. Respirology 2000; 5(3): 271-5.

  • 9. Van Hirtum A, Berckmans D. Automated recognition of spontaneous versus voluntary cough. Med Eng Phys 2002; 24(7-8): 541-510. Murata A, Ohota N, Shibuya A, Ono H, Kudoh S. New non-invasive automatic cough counting program based on 6 types of classified cough sounds. Intern Med 2006; 45(6): 391-7.

  • 10. Murata A, Ohota N, Shibuya A, Ono H, Kudoh S. New non-invasive automatic cough counting program based on 6 types of classified cough sounds. Intern Med 2006; 45(6):391-7.

  • 11. Martinek J, Tatar M, Javorka M. Distinction between voluntary cough sound and speech in volunteers by spectral and complexity analysis. J Physiol Pharmacol 2008; 59(6): 433-40.

  • 12. Shin SH, Hashimoto T, Hatano S. Automatic detection system for cough sounds as a symptom of abnormal health condition. IEEE Trans Inf Technol Biomed 2009; 13(4): 486-93.

  • 13. Martinek J, Bencova A, Tatar M, Vrabec M, Zatko T, Javorka M. Examination of cough and non-cough sounds by spectral and complexity analysis in patients suffering from respiratory diseases. Acta Med Mart 2009; 9(3): 12-17.

  • 14. Martinek J, Zatko T, Tatar M, Javotka M. Distinction of cough from other sounds produced by daily activities in the upper airways. Bratisl Lek Listy 2011; 112(3): 120-4.

  • 15. Korpas J, Sadlonova J, Vrabec M. Analysis of the cough sound: an overview. Pulm Pharmacol 1996; 9(5-6): 261-8.

  • 16. Klco P, Martinek J, Tatar M, Javorka M. Application of the artificial neural networks for the cough sound classification (in Slovak). New knowledge in respirology (the lectures in XXII. Martin’s Day of Breathing).

  • 17. Martinek J, Tatar M, Javorka M. An Adjusted algorithm for cough sound analysis in patients with respiratory diseases (in Slovak). New knowledges in respirology (the lectures in XXII. Martin’s Day of Breathing).

  • 18. Vizel E, Yigla M, Goryachev Y, Dekel E, Felis V, Levi H, Kroin I, Godfrey S, Gavriely N. Validation of an ambulatoty cough detection and counting application using voluntary cough under different conditions. Cough 2010; 6:3.

Acta Medica Martiniana

The Journal of Comenius University in Bratislava

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 9 9 9
PDF Downloads 4 4 4