Smart Digital Stethoscope Using Artificial Intelligence, Machine Learning, and IoT

D. S. S. N. Raju; Medisetty Harshitha; Doppa Parimala; Jeeru Venkata Chakradhar Reddy; Anguru Krishna Vamsi

doi:10.62762/NGCST.2026.345581

Article Information

Published in Next-Generation Computing Systems and Technologies

Volume/Issue Volume 2, Issue 2, 2026

Pages 59-63

Abstract

There is an increase in heart diseases at a very high rate in today's world, and therefore early detection becomes a necessity in the treatment of such cases. In this paper, we present a digital stethoscope with AI and ML that records heart sounds automatically. Phonocardiogram (PCG) signals are recorded by the system, and the recorded signals are then processed using filtering, normalization, and peak detection to obtain features including heart sounds S1 and S2. The extracted features are fed into the classification algorithm, which in this case is the Random Forest Classifier, and they are classified as either abnormal or normal heart conditions. IoT is implemented in the system to ensure storage of data. Experimental tests carried out have shown that the system can detect abnormalities such as irregular heart rates and murmurs.

Graphical Abstract

Smart Digital Stethoscope Using Artificial Intelligence, Machine Learning, and IoT

Keywords

heart sound analysis digital stethoscope machine learning signal processing IoT PCG

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Real-time heart sound recordings were collected from volunteer participants under informed consent. As this constitutes minimal-risk research involving non-invasive acoustic recording only, formal institutional ethics review was waived in accordance with institutional guidelines.

References

Tavel, M. E. (2006). Cardiac auscultation: a glorious past—and it does have a future!. Circulation, 113(9), 1255-1259.
[CrossRef] [Google Scholar]
Chakir, F., Jilbab, A., Nacir, C., & Hammouch, A. (2018). Phonocardiogram signals processing approach for PASCAL classifying heart sounds challenge. Signal, Image and Video Processing, 12(6), 1149-1155.
[CrossRef] [Google Scholar]
Springer, D. B., Tarassenko, L., & Clifford, G. D. (2015). Logistic regression-HSMM-based heart sound segmentation. IEEE transactions on biomedical engineering, 63(4), 822-832.
[CrossRef] [Google Scholar]
Schmidt, S. E., Holst-Hansen, C., Graff, C., Toft, E., & Struijk, J. J. (2010). Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiological measurement, 31(4), 513-529.
[CrossRef] [Google Scholar]
Durand, L. G., & Pibarot, P. (1995). Digital signal processing of the phonocardiogram: review of the most recent advancements. Critical Reviews™ in biomedical engineering, 23(3-4).
[CrossRef] [Google Scholar]
Liu, C., Springer, D., Li, Q., Moody, B., Juan, R. A., Chorro, F. J., ... & Clifford, G. D. (2016). An open access database for the evaluation of heart sound algorithms. Physiological measurement, 37(12), 2181-2213.
[CrossRef] [Google Scholar]
Maglogiannis, I., Loukis, E., Zafiropoulos, E., & Stasis, A. (2009). Support vectors machine-based identification of heart valve diseases using heart sounds. Computer methods and programs in biomedicine, 95(1), 47-61.
[CrossRef] [Google Scholar]
Bhatikar, S. R., DeGroff, C., & Mahajan, R. L. (2005). A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics. Artificial intelligence in medicine, 33(3), 251-260.
[CrossRef] [Google Scholar]
Potes, C., Parvaneh, S., Rahman, A., & Conroy, B. (2016, September). Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. In 2016 computing in cardiology conference (CinC) (pp. 621-624). IEEE.
[Google Scholar]
Rubin, J., Abreu, R., Ganguli, A., Nelaturi, S., Matei, I., & Sricharan, K. (2017). Recognizing abnormal heart sounds using deep learning. arXiv preprint arXiv:1707.04642.
[CrossRef] [Google Scholar]
Rangayyan, R. M. (2002). Biomedical Signal Analysis: A Case-Study Approach. IEEE Signal Processing Magazine, 19(4), 86-86.
[CrossRef] [Google Scholar]
Liang, H., Lukkarinen, S., & Hartimo, I. (1997, September). Heart sound segmentation algorithm based on heart sound envelogram. In Computers in Cardiology 1997 (pp. 105-108). IEEE.
[CrossRef] [Google Scholar]
El-Segaier, M., Lilja, O., Lukkarinen, S., Sörnmo, L., Sepponen, R., & Pesonen, E. (2005). Computer-based detection and analysis of heart sound and murmur. Annals of biomedical engineering, 33(7), 937-942.
[CrossRef] [Google Scholar]

Cite This Article

APA Style

Raju, D. S. S. N., Harshitha, M., Parimala, D., Reddy, J. V. C., & Vamsi, A. K. (2026). Smart Digital Stethoscope Using Artificial Intelligence, Machine Learning, and IoT. Next-Generation Computing Systems and Technologies, 2(2), 59-63. https://doi.org/10.62762/NGCST.2026.345581

Export Citation

RIS Format

Compatible with EndNote, Zotero, Mendeley, and other reference managers

TY - JOUR
AU - Raju, D. S. S. N.
AU - Harshitha, Medisetty
AU - Parimala, Doppa
AU - Reddy, Jeeru Venkata Chakradhar
AU - Vamsi, Anguru Krishna
PY - 2026
DA - 2026/06/27
TI - Smart Digital Stethoscope Using Artificial Intelligence, Machine Learning, and IoT
JO - Next-Generation Computing Systems and Technologies
T2 - Next-Generation Computing Systems and Technologies
JF - Next-Generation Computing Systems and Technologies
VL - 2
IS - 2
SP - 59
EP - 63
DO - 10.62762/NGCST.2026.345581
UR - https://www.icck.org/article/abs/NGCST.2026.345581
KW - heart sound analysis
KW - digital stethoscope
KW - machine learning
KW - signal processing
KW - IoT
KW - PCG
AB - There is an increase in heart diseases at a very high rate in today's world, and therefore early detection becomes a necessity in the treatment of such cases. In this paper, we present a digital stethoscope with AI and ML that records heart sounds automatically. Phonocardiogram (PCG) signals are recorded by the system, and the recorded signals are then processed using filtering, normalization, and peak detection to obtain features including heart sounds S1 and S2. The extracted features are fed into the classification algorithm, which in this case is the Random Forest Classifier, and they are classified as either abnormal or normal heart conditions. IoT is implemented in the system to ensure storage of data. Experimental tests carried out have shown that the system can detect abnormalities such as irregular heart rates and murmurs.
SN - 3070-3328
PB - Institute of Central Computation and Knowledge
LA - English
ER -

BibTeX Format

Compatible with LaTeX, BibTeX, and other reference managers

@article{Raju2026Smart,
  author = {D. S. S. N. Raju and Medisetty Harshitha and Doppa Parimala and Jeeru Venkata Chakradhar Reddy and Anguru Krishna Vamsi},
  title = {Smart Digital Stethoscope Using Artificial Intelligence, Machine Learning, and IoT},
  journal = {Next-Generation Computing Systems and Technologies},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {59-63},
  doi = {10.62762/NGCST.2026.345581},
  url = {https://www.icck.org/article/abs/NGCST.2026.345581},
  abstract = {There is an increase in heart diseases at a very high rate in today's world, and therefore early detection becomes a necessity in the treatment of such cases. In this paper, we present a digital stethoscope with AI and ML that records heart sounds automatically. Phonocardiogram (PCG) signals are recorded by the system, and the recorded signals are then processed using filtering, normalization, and peak detection to obtain features including heart sounds S1 and S2. The extracted features are fed into the classification algorithm, which in this case is the Random Forest Classifier, and they are classified as either abnormal or normal heart conditions. IoT is implemented in the system to ensure storage of data. Experimental tests carried out have shown that the system can detect abnormalities such as irregular heart rates and murmurs.},
  keywords = {heart sound analysis, digital stethoscope, machine learning, signal processing, IoT, PCG},
  issn = {3070-3328},
  publisher = {Institute of Central Computation and Knowledge}
}

Article Metrics

Citations

Crossref

0

Scopus

0

Views

14

PDF Downloads

3

Publisher's Note

ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and Permissions

Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

Next-Generation Computing Systems and Technologies

ISSN: 3070-3328 (Online)

[email protected]

Preserved at
Portico

User

Unlimited Downloads

Complete Library Access

Membership Eligibility

Community Leadership Opportunities