Characterization of Cough Sounds Based on Measured Sound Pressure Levels from Arduino-Based MAX9814 Sound Sensor

Aris widodo, Muktamar Cholifah Aisiyah

Abstract


Research has been conducted on the characterization of coughing sounds based on the measured sound pressure level of the Arduino-based MAX9814 sound sensor to determine the characteristics of coughing sounds based on the Sound Pressure Level (SPL). This research method is carried out by designing sound sensor hardware and software using the MAX9814 sound sensor. After that, recording the coughing sound data from the cough voice coswara respondent data set using a sound sensor and converting it to SPL data. After that, the cough SPL data was analyzed from the SPL cycle graph during recording and the cough phase pattern. And the resulting characterization of coughing sound based on coughing pressure using an Arduino-based MAX9814 sound sensor produced a coughing sound character in terms of sound pressure level (SPL) based on the expulsive phase and the intermediate phase of coughing. The expulsive phase indicator is emphasized the intensity of occurrence and density in one cycle of recording coughing sounds. And for the intermediate phase based on the drop rate of the SPL back to the SPL position without coughing. The SPL of cough detected by the MAX9814 sound sensor is ±80dB.

 

 


Keywords


Characterization; Cough; Sensor; Sound Presure Level

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References


Sharma, N., Krishnan, P., Kumar, R., Ramoji, S., Chetupalli, S. R., Ghosh, P. K., & Ganapathy, S. (2020). Coswara--a database of breathing, cough, and voice sounds for COVID-19 diagnosis. arXiv preprint arXiv:2005.10548.

A. Chang and M. P. Karnell, “Perceived phonatory effort and phonation threshold pressure across a prolonged voice loading task: a study of vocal fatigue,” Journal of Voice, vol. 18, no. 4, pp. 454–466, 2004.

W. Thorpe, M. Kurver, G. King, and C. Salome, “Acoustic analysis of cough,” in The Seventh Australian and New Zealand Intelligent Information Systems Conference. IEEE, 2001, pp. 391–394.

M. Polverino, F. Polverino, M. Fasolino, F. And `o, A. Alfieri, andF. De Blasio, “Anatomy and neuro-pathophysiology of the cough reflex arc,” Multidisciplinary respiratory medicine, vol. 7, no. 1, p. 5, 2012.

Coryllos, P.N. Action of the Diaphragm in Cough: Experimental and Clinical Study on the Human. Am. J. Med. Sci. 1937, 194, 523–535.

Drugman, T.; Urbain, J.; Bauwens, N.; Chessini, R.; Valderrama, C.; Lebecque, P.; Dutoit, T. Objective Study of Sensor Relevance for Automatic Cough Detection. IEEE J. Biomed. Health Inform. 2013, 17, 699–707.

Morice, A.H.; Fontana, G.A.; Belvisi, M.G.; Birring, S.S.; Chung, K.F.; Dicpinigaitis, P.V.; Kastelik, J.A.; McGarveyTatar, M.; et al. ERS Guidelines on the Assessment of Cough. Eur. Respir. J. 2007, 29, 1256–1276.

C. Pham, “Mobicough: Real-time cough detection and monitoring using low-cost mobile devices,” in Intelligent Information and Database Systems, N. T. Nguyen, B. Trawi´nski, H. Fujita, and T.P. Hong, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016, pp. 300–309.

T. Otoshi et al., “A novel automatic cough frequency monitoring system combining a triaxial accelerometer and a stretchable strain sensor,” Sci. Reports 2021 111, vol. 11, no. 1, pp. 1–9, May 2021, doi: 10.1038/s41598-021-89457-0.

Y. Shi, H. Liu, Y. Wang, M. Cai, and W. Xu, “Theory and application of audio-based assessment of cough,” J. Sensors, vol. 2018, 2018, doi: 10.1155/2018/9845321.

J. Monge-Alvarez, C. Hoyos-Barcelo, P. Lesso, and P. Casaseca-De-La-Higuera, “Robust Detection of Audio-Cough Events Using Local Hu Moments,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 1, pp. 184–196, Jan. 2019, doi: 10.1109/JBHI.2018.2800741.

J. Amoh and K. Odame, “Deep Neural Networks for Identifying Cough Sounds,” IEEE Trans. Biomed. Circuits Syst., vol. 10, no. 5, pp. 1003–1011, Oct. 2016, doi: 10.1109/TBCAS.2016.2598794.

J. Laguarta, F. Hueto, and B. Subirana, “COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings,” IEEE Open J. Eng. Med. Biol., vol. 1, pp. 275–281, 2020, doi: 10.1109/OJEMB.2020.3026928.

Madhurananda, P., Marisa, K., Byron, R., Robin, W., Grant, T., & Thomas, N. (2021). Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment. arXiv preprint arXiv:2103.13300.




DOI: http://dx.doi.org/10.12962/j24604682.v19i1.14432

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