Main Article Content

Abstract

Healthcare monitoring system have received substantial attention of researchers in the past few years. The most important aim was to invent a dedicated patient monitoring system in order to make it possible for healthcare professionals to supervise their patients from a remote distance. The most commonly used technique to identify or assess the cardiac disorders is body Auscultation. This is an inexpensive and highly effective method, but presence of professional doctors is always necessary for this method to understand heart sound for diagnosis of cardiac disorders. This work aims to provide the basic distinction between healthy and unhealthy heart sounds. some of the unique features of heart sound are to be taken into considerations to automate the identification of cardiac disorders. The experimental results should specify enough differentiation between healthy and unhealthy patient’s data that is heart sounds for automated assessment of cardiac disorders using various signal processing algorithms.

Keywords

auscultation; digital stethoscope; signal processing; cardiac disorders; heart sound, Lub, Dub

Article Details

How to Cite
[1]
Pranali K. Jawale, Dr. A. N. Cheeran, and Vaibhav Awandekar, “ANALYSIS OF HEART SOUND FOR AUTOMATED DIAGNOSIS OF CARDIAC DISORDERS”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 5, p. 7, Jun. 2020.

References

  1. Roy, Soumya, and Rajarshi Gupta. "Short range centralized cardiac health monitoring system based on zigbee communication." In Global Humanitarian Technology Conference-South Asia Satellite (GHTC-SAS), 2014 IEEE, pp. 177-182. IEEE, 2014.
  2. Kakria, Priyanka, N. K. Tripathi, and Peerapong Kitipawang. "A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors." International journal of telemedicine and applications 2015 (2015): 8.
  3. V. Prasad and G. Phade, “Design of Electronic Stethoscope and Heart Rate Monitor for Remote Area Application,” International Journal of Computer Applications, vol. 137, no. 10, pp. 33–36, Mar. 2016.
  4. Shaikh, Rubina A. "Real time health monitoring system of remote patient using ARM7." Control and Automation (IJICA) ISSN(2012): 2231-1890.
  5. Sunehra, Dhiraj, and Pini Ramakrishna. "Web based patient health monitoring system using Raspberry Pi." In Contemporary Computing and Informatics (IC3I), 2016 2nd International Conference on, pp. 568-574. IEEE, 2016.
  6. A. Singh, M. K. Dutta, and C. M. Travieso, “Analysis of Heart Sound for Automated Diagnosis of Cardiac Disorders.,” 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), 2017.
  7. M. Mishra, A. Singh, M. K. Dutta, and A. R. Munoz, “Automatic screening of cardiac disorders using wavelet analysis of heart sound,” 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), 2017.
  8. K. Hassani, K. Bajelani, M. Navidbakhsh, D. Doyle, and F. Taherian, “Heart sound segmentation based on homomorphic filtering,” Perfusion, vol. 29, no. 4, pp. 351–359, Feb. 2014
  9. Y.-L. Tseng, P.-Y. Ko, and F.-S. Jaw, “Detection of the third and fourth heart sounds using Hilbert-Huang transform,” BioMedical Engineering OnLine,vol.11,no.1,p.8,2012
  10. J. S. Sonawane and D. R. Patil, “Prediction of heart disease using learning vector quantization algorithm,” 2014 Conference on IT in Business, Industry and Government (CSIBIG), 2014.
  11. F. Chakir, A. Jilbab, C. Nacir, and A. Hammouch, “Phonocardiogram signals classification into normal heart sounds and heart murmur sounds,” 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA), 2016.
  12. A. Hamidah, R. Saputra, T. L. R. Mengko, R. Mengko, and B. Anggoro, “Effective heart sounds detection method based on signals
  13. characteristics,” 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2016

Similar Articles

You may also start an advanced similarity search for this article.