Author(s)

Cheng Su ^1,2, Lei Zhang ^1,2, Shiyu Wei ², Yongkang Wang ³, Chang Liu ^2,4, Yong Wan ⁵

Affiliation(s)

¹ School of Future Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710000, China
² Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710000, China
³ Department of Hepatobiliary Surgery, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710000, China
⁴ Department of Hepatobiliary and Liver Transplantation, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710000, China
⁵ Department of Geriatrics, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710000, China

Corresponding Author

Yong Wan

ABSTRACT

Cardiopulmonary diseases pose a severe threat to human health, driving the digital innovation of auscultation technology. Traditional stethoscopes suffer from limitations such as non-real-time monitoring and non-storable data. This study designs and implements a wireless stethoscope system based on embedded software, integrating analog electronics, microcontroller technology, and modern network communication technology to construct a portable hardware acquisition device and a Web real-time display platform with high signal-to-noise ratio and low distortion. The hardware uses STM32F405 as the main controller, combined with VS1053 audio codec chip and ESP8266 WIFI module, enabling 16-bit mono, 8kHz sampling rate acquisition of heart and breath sounds and wireless transmission via TCP protocol. The software, developed with embedded C language, SpringBoot, and React framework, achieves real-time processing, parsing, storage, and waveform visualization of audio data. Tests show that the system has an audio acquisition delay ≤3 seconds, a similarity of 0.95 with standard audio, and realizes heart sound denoising through wavelet transform algorithm, effectively improving clinical diagnostic data quality. This system provides a practical solution for remote auscultation, medical resource optimization, and cardiopulmonary sound research, demonstrating significant application prospects and social value.

KEYWORDS

Auscultation System; Heart Sounds; Breathing Sounds

CITE THIS PAPER

Cheng Su, Lei Zhang, Shiyu Wei, Yongkang Wang, Chang Liu, Yong Wan, Wireless Stethoscope System Based on Embedded Software. MEDS Public Health and Preventive Medicine (2025) Vol. 5: 34-40. DOI: http://dx.doi.org/10.23977/phpm.2025.050206.

REFERENCES

[1] Dong Lichao, Guo Xingming, and Zheng Yinen. Research on a Wavelet Packet Denoising Algorithm for Heart Sound Signals Based on CEEMD. Journal of Vibration and Shock, 2019; 38 (9):192-222.
[2] The W. Report on Cardiovascular Health and Diseases in China 2022: an Updated Summary. Biomed Environ Sci. 2023;36(8):669-701.
[3] A S C , M B S , Mona B , et al. Chronic obstructive pulmonary disease. [J]. Lancet (London, England), 2022, 399(10342): 2227-2242.
[4] Roff M, Slifirski O, Grooby E, Marzbanrad F, Malhotra A. Digital Stethoscope Use in Neonates: A Systematic Review. Newborn. 2023;2:235-243.
[5] Roguin A. Rene Theophile Hyacinthe Laënnec (1781-1826): the man behind the stethoscope. Clin Med Res. 2006;4(3):230-5.
[6] Sarkar M, Madabhavi I, Niranjan N, Dogra M. Auscultation of the respiratory system. Ann Thorac Med. 2015;10(3):158-168.
[7] Guidelines for Infection Prevention and Control in Sonography: Reprocessing the Ultrasound Transducer. Journal of Diagnostic Medical Sonography. 2020;36(4):381-403.
[8] Zhao QM, Niu C, Liu F, Wu L, Ma XJ, Huang GY. Accuracy of cardiac auscultation in detection of neonatal congenital heart disease by general paediatricians. Cardiol Young. 2019;29(5):679-683.
[9] Springer DB, Tarassenko L, Clifford GD. Logistic Regression-HSMM-Based Heart Sound Segmentation. IEEE Transactions on Biomedical Engineering. 2016;63(4):822-832.
[10] Kue R, Brown P, Ness C, Scheulen J. Adverse clinical events during intrahospital transport by a specialized team: a preliminary report. Am J Crit Care. 2011;20(2):153-161; quiz 162.
[11] Khan FA, Abid A, Khan MS. Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features. Physiol Meas. 2020;41(5):055006.
[12] Zhao Feng, Hu Nan, Chen Yan, Cai Shengsheng. Quantitative Analysis and Application Evaluation of Respiratory Sounds Based on Intelligent Cardiopulmonary Sound Diagnosis. Chinese Digital Medicine. 2024; 19(8):74-78.
[13] Li H, Wang J, Zhao S, Tian F, Yang J, Sawan M, editors. Real-time Biosignal Recording and Machine-Learning Analysis System. 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS); 2022 13-15 June 2022.

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