Budi Yanti


Introduction: Wheezing is one of the most common manifestations of airway obstruction.  The use of a stethoscope in the wheezing examination has several disadvantages such as subjective results  and depends on  the auditor's  hearing sensitivity.  So an easy device is needed that helps determine the wheezing  sound precisely.  This study assembled a single tool to detect wheezing  sounds based on the internet of things.

Method: This tool is designed with a microprocessor hardware connected to  an electric stethoscope so that it can be attached to the patient's chest  wall.  Collection of chest breathing voice data  accessed on  The creation of algorithms with Convolutional Neural Networks (CNN)  was later changed to Mel Frequency Cepstral Coefficients (MFCC). This model  will be implanted in a microprocessor and use python  language to  be able to record  the sound of chest  wall vibrations.  The recorded sound is converted into MFCC  to make it easier to perform   wheezing sound detection.  MFCC image results and  detection results are sent to the database via  the firebase database feature which stores MFCC  photos in real-time as they are detected.  Designing android application software using Flutter   builds communication between android  applications and firebase databases that allows applications to  retrieve MFCC  images as the final result.

Result: The results of  the tool trial on five volunteers, three exacerbation asthma patients and two healthy people  showed the device can detect wheezing  sounds at a frequency of  400Hz with 80%  accuracy through CNN and MFCC  algorithms  Internet of things based.

Conclusion: This tool can help health workers  to accurately determine wheezing   sounds, enforce the diagnosis   faster, the prognosis of the disease to be  better, so as to  reduce the number  morbidity and mortality of diseases with airway abnormalities in Indonesia



Wheezing, Detection, Internet of Things

Full Text:



  1. Sarkar M, Madabhavi I, Niranjan N, Dogra M. Auscultation of the respiratory system. Ann Thorac Med. 2015;10(3):158–68.
  2. Patel PH, Mirabile VS, Sharma S MC. Wheezing (Nursing). StatPearls [Internet]. 2021. p. Treasure Island (FL): StatPearls Publishing; 2021.
  3. Nagasaka Y. Lung sounds in bronchial asthma. Allergol Int [Internet]. 2012;61(3):353–63. Available from:
  4. Rao A, Huynh E, Royston TJ, Kornblith A, Roy S. Acoustic Methods for Pulmonary Diagnosis. IEEE Rev Biomed Eng. 2019;12:221–39.
  5. Zulfiqar R, Majeed F, Irfan R, Rauf HT, Benkhelifa E, Belkacem AN. Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition. Front Med. 2021;8( November):1–16.
  6. Pramono RXA, Bowyer S, Rodriguez-Villegas E. Automatic adventitious respiratory sound analysis: A systematic review. Vol. 12, PLoS ONE. 2017. 1–43 p.
  7. Hafke-Dys H, Brȩborowicz A, Kleka P, Kociński J, Biniakowski A. The accuracy of lung auscultation in the practice of physicians and medical students. PLoS One. 2019;14(8).
  8. Maulidin L. Spectral Analysis of Abnormal Respiratory Sounds In Children With Pneumonia. 2018.
  9. Li SH, Lin BS, Tsai CH, Yang CT, Lin BS. Design of wearable breathing sound monitoring system for real-time wheeze detection. Sensors (Switzerland). 2017;17(1).
  10. Zimmerman B, Williams D. Lung Sounds. StatPearls [Internet]. 2021. p.
  11. Reichert S, Gass R, Brandt C, Andrès E. Analysis of Respiratory Sounds: State of the Art. Clin Med Circ Respirat Pulm Med. 2008;2:CCRPM. S530.
  12. Henderson J, Granell R, Heron J, Sheriff A, Simpson A, Woodcock A, et al. Associations of wheezing phenotypes in the first 6 years of life with atopy, lung function and airway responsiveness in mid-childhood. Thorax. 2008;63(11):974–80.
  13. Uwaoma C, Mansingh G. Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device. Int J Appl Inf Syst. 2014;7(11):35–40.
  14. Jin F, Krishnan S, Sattar F. Adventitious sounds identification and extraction using temporal-spectral dominance-based features. IEEE Trans Biomed Eng. 2011;58(11):3078–87.
  15. Cruz JDLT, Quesada FJC, Reyes NR, Galán SG, Orti JJC, Chica GP. Monophonic and polyphonic wheezing classification based on constrained low-rank non-negative matrix factorization. Sensors. 2021;21(5):1–23.
  16. Palaniappan R, Sundaraj K, Sundaraj S. Artificial intelligence techniques used in respiratory sound analysis - A systematic review. Biomed Tech. 2014;59(1):7–18.
  17. Preece SJ, Goulermas JY, Kenney LPJ, Howard D. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng. 2009;56(3):871–9.
  18. Purnama IBI, Sumertayasa IM. The implementation of Fast Hilbert Transform with low pass filter for reconstructing breathing sound signals in LabView. J Phys Conf Ser. 2020;1450(1).


  • There are currently no refbacks.