ประสิทธิภาพของสัญญาณเสียงจากหูฟังเสียงแบบดิจิทัลไร้สายแบบเรียลไทม์เปรียบเทียบกับหูฟังมาตรฐานในการประเมินระบบหัวใจและปอด

Porntip Nimkuntod; Pattama Tongdee; Peerapong Uthansakul; Nuntawut Kaoungku; Monthippa Uthansaku; Talit Jumphoo; Sarunya Kanjanawattana; Parin Sornlertlamvanich; Komsan Srivisut; Chontida Krobpornchai

Authors

Porntip Nimkuntod Institute of Medicine, Suranaree University of Technology and Suranaree University of Technology Hospital, Suranaree University of Technology
Pattama Tongdee Institute of Medicine, Suranaree University of Technology and Suranaree University of Technology Hospital, Suranaree University of Technology
Peerapong Uthansakul Institute of Engineering, Suranaree University of Technology
Nuntawut Kaoungku Institute of Medicine, Suranaree University of Technology
Monthippa Uthansaku Institute of Engineering, Suranaree University of Technology
Talit Jumphoo Institute of Engineering, Suranaree University of Technology
Sarunya Kanjanawattana Institute of Engineering, Suranaree University of Technology
Parin Sornlertlamvanich Institute of Engineering, Suranaree University of Technology
Komsan Srivisut Institute of Engineering, Suranaree University of Technology
Chontida Krobpornchai Institute of Engineering, Suranaree University of Technology

Keywords:

Audio Signals, Wireless Digital Stethoscope Real Time Transition, Standard Stethoscope

Abstract

This study aimed to develop a medical technology innovation, specifically a prototype telemedicine stethoscope capable of transmitting real-time audio signals via the internet. A quasi-experimental design was employed to compare two population groups, with the objective of evaluating the performance of a remote heart and lung auscultation device integrated with a real-time sound analysis system. Data were collected from 108 participants divided into two groups. Group 1 consisted of 73 healthy individuals aged 18 years and older with no history of cardiovascular disease. Group 2 included 35 patients with cardiovascular and/or respiratory conditions who were hospitalized at Suranaree University of Technology Hospital. Data collection was conducted between 1 February 2024 and 31 May 2025. General demographic characteristics and physical examination findings were obtained, including heart and lung auscultation at 14 chest wall positions.

The results showed that participants in the normal group were healthy volunteers with normal heart and lung sounds, with a mean age of 26.00 ± 8.15 years, 98.6% male, and a mean body mass index (BMI) of 23.66 ± 7.53 kg/m². In the cardiovascular/respiratory disease group, the mean age was 68.83 ± 11.71 years, 48.57% male, with a mean BMI of 24.57 ± 2.99 kg/m². The performance of the audio signals, evaluated across three sound quality domains (clarity, loudness, and accuracy of heart and lung sounds), was superior to that of a standard stethoscope in both groups. When comparing the normal group and the group with abnormal sounds, no statistically significant difference in sound quality was observed.

References

McMehan C., So P., Li K.F., Jasechko G., Poulin M. Electronic Stethoscope for eHealth and Telemedicine. CMBES Proc 2010; 33.

Hailey D. Some successes and limitations with telehealth in Canada. J. Telemed. Telecare 2001; 7((Suppl. S2)) : 73–5.

Poon Z., Tan N.C. A qualitative research study of primary care physicians’ views of telehealth in delivering postnatal care to women. BMC Prim. Care 2022; 23 : 206.

Kwon H., An S., Lee H.-Y., Cha W.C., Kim S., Cho M., Kong H.-J. Review of Smart Hospital Services in Real Healthcare Environments. Health Inform. Res 2022; 28 : 3–15.

Seah JJ, Zhao J, Wang Y, Lee HP. Review on the Advancements of Stethoscope Types in Chest Auscultation. Diagnostics (Basel) 2023;13(9):1545.

Foche-Perez I, Ramirez-Payba R, Hirigoyen-Emparanza G, Balducci-Gonzalez F, Simo-Reigadas FJ, Seoane-Pascual J, et al. An open real-time tele-stethoscopy system. Biomed Eng Online 2012;11:57.

Kelmenson DA, Heath JK, Ball SA, Kaafarani HM, Baker EM, Yeh DD, et al. Prototype electronic stethoscope vs. conventional stethoscope for auscultation of heart sounds.J Med Eng Technol 2014;38:307–10.

Silverman B, Balk M. Digital stethoscope-improved auscultation at the bedside. Am J Cardiol 2019;123:984–5.

Koning C, Lock A. A systematic review and utilization study of digital stethoscopes for cardiopulmonary assessments. J Med Res Innov 2021; 5(40): 4–14.

Swarup S, Makaryus AN. Digital stethoscope: technology update. Med Devices (Auckl) 2018; 11 : 29–36.

Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A. Computerised analysis of telemonitored respiratory sounds for predicting acute exacerbations of COPD. Sensors (Basel) 2015; 15 : 26978–96.

Altan G, Kutlu Y, Allahverdi N. Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE J Biomed Health Inform 2020; 24: 1344–50.

Mondal A, Banerjee P, Tang H. A novel feature extraction technique for pulmonary sound analysis based on EMD. Comput Methods Programs Biomed 2018; 159 :199–209.

Jung SY, Liao CH, Wu YS, Yuan SM, Sun CT. Efficiently classifying lung sounds through depthwise separable CNN models with fused STFT and MFCC features. Diagnostics (Basel) 2021; 11 : 732.

Meng F, Shi Y, Wang N, Cai M, Luo Z. Detection of respiratory sounds based on wavelet coefficients and machine learning. IEEE Access 2020; 8: 155710–20.

Gurung A, Scrafford CG, Tielsch JM, Levine OS, Checkley W. Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. Respir Med 2011;105: 1396–403.

Palaniappan R, Sundaraj K, Sundaraj S. Artificial intelligence techniques used in respiratory sound analysis: a systematic review. Biomed Tech (Berl) 2014; 59 : 7–18.

Grzywalski T, Piecuch M, Szajek M, Breborowicz A, Hafke-Dys H, Kocinski J, et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. Eur J Pediatr 2019; 178 : 883–90.

Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR. Application of semi-supervised deep learning to lung sound analysis. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2016 : 804–7.

Murphy RL, Vyshedskiy A, Power-Charnitsky VA, Bana DS, Marinelli PM, Wong-Tse A, et al. Automated lung sound analysis in patients with pneumonia. Respir Care 2004; 49 : 1490–7.