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A proposed algorithm for analysing heart sounds and calculating their time intervals

  • Abdulrahman K. Eesee EMAIL logo , Hassan M. Qassim and Mothanna Sh. Aziz
Published/Copyright: March 5, 2020
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Abstract

Heart sounds play a crucial role in the clinical assessment of patients. Stethoscopes are used for detecting heart sounds and diagnosing potential abnormal conditions. However, several parameters of the cardiac sounds cannot be extracted by traditional stethoscopes. This paper presents a proposed algorithm based on peaks detection. Besides its ability of filtering the heart sounds signals, the time intervals of these sounds in addition to the heart rate were calculated by the proposed algorithm in an efficient way. Signals of the heart sounds from two sources were used to evaluate the efficiency of the algorithm. The first source was the data recorded from 14 participants, whereas the second source was the free data set sponsored by PASCAL. The algorithm showed different performance accuracy for detecting the main heart sounds based on the source of the data used in the study. The accuracy was 93.6% when using the data recorded from the first source, whereas it was 76.194% for the data of the second source.

Acknowledgment

The researcher group would like to thank Dr. Saad K. Eesee for his support and valuable scientific advices during the implementation of this research.

  1. Ethical Approval: The conducted research is not related to either human or animal use.

  2. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Research funding: None declared.

  4. Employment or leadership: None declared.

  5. Honorarium: None declared.

  6. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  7. Conflict of interest: The authors declare that they have no conflict of interest.

References

[1] Martini FH, Nath JL, Bartholomew EF. Fundamentals of anatomy and physiology, 9th ed. San Francisco: Pearson Education, 2012.Search in Google Scholar

[2] Potes C, Parvaneh S, Rahman A, Conroy B. Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. 2016 Computing in Cardiology Conference (CinC). Vancouver, BC, Canada: IEEE, 2016:621-4. Available from: https://ieeexplore.ieee.org/abstract/document/7868819.10.22489/CinC.2016.182-399Search in Google Scholar

[3] Varghees VN, Ramachandran KI. A novel heart sound activity detection framework for automated heart sound analysis. Biomed Signal Process Control 2014;13:174–88.10.1016/j.bspc.2014.05.002Search in Google Scholar

[4] Gamero L, Watrous R. Detection of the first and second heart sound using probabilistic models. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat No03CH37439). Cancun, Mexico: IEEE, 2003:2877-80. Available from: https://ieeexplore.ieee.org/abstract/document/1280519.10.1109/IEMBS.2003.1280519Search in Google Scholar

[5] Wang H, Chen J, Hu Y, Jiang Z, Samjin C. Heart sound measurement and analysis system with digital stethoscope. 2009 2nd International Conference on Biomedical Engineering and Informatics. Tianjin, China: IEEE, 2009:1-5. Available from: https://ieeexplore.ieee.org/abstract/document/5305287.10.1109/BMEI.2009.5305287Search in Google Scholar

[6] El-Segaier M, Lilja O, Lukkarinen S, Sörnmo L, Sepponen R, Pesonen E. Computer-based detection and analysis of heart sound and murmur. Ann Biomed Eng 2005;33:937–42.10.1007/s10439-005-4053-3Search in Google Scholar PubMed

[7] Garcia TB, Garcia DJ. Arrhythmia recognition: the art of interpretation. Burlington, MA: Jones & Bartlett Publishers, 2019.Search in Google Scholar

[8] Sillanmäki S, Lipponen JA, Tarvainen MP, Laitinen T, Hedman M, Hedman A, et al. Relationships between electrical and mechanical dyssynchrony in patients with left bundle branch block and healthy controls. J Nucl Cardiol 2019;26:1228–39.10.1007/s12350-018-1204-0Search in Google Scholar PubMed

[9] UCSFHealth.org. Complete Heart Block, University of California San Francisco. Available at: https://www.ucsfhealth.org/conditions/complete_heart_block/. Accessed: 7 Sep 2019.Search in Google Scholar

[10] Giordano N, Knaflitz M. A novel method for measuring the timing of heart sound components through digital phonocardiography. Sensors 2019;19:1868.10.3390/s19081868Search in Google Scholar PubMed PubMed Central

[11] Kamran H, Salciccioli L, Pushilin S, Kumar P, Carter J, Kuo J, et al. Characterization of cardiac time intervals in healthy bonnet macaques (Macaca radiata) by using an electronic stethoscope. J Am Assoc Lab Anim Sci 2011;50:238–43.Search in Google Scholar

[12] Kumar D, Carvalho P, Antunes M, Henriques J, Eugenio L, Schmidt R et al. Detection of S1 and S2 heart sounds by high frequency signatures. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society [Internet]. New York, NY, USA: IEEE, 2006:1410-6. [cited 18 February 2020]. Available from: https://ieeexplore.ieee.org/abstract/document/4462026.10.1109/IEMBS.2006.260735Search in Google Scholar

[13] Dao AT. Wireless laptop-based phonocardiograph and diagnosis. PeerJ 2015;3:e1178.10.7717/peerj.1178Search in Google Scholar PubMed PubMed Central

[14] Stainton S, Tsimenidis C, Murray A. Characteristics of phonocardiography waveforms that influence automatic feature recognition. 2016 Computing in Cardiology Conference (CinC). Vancouver, BC, Canada: IEEE, 2016:1173-6. Available from: https://ieeexplore.ieee.org/abstract/document/7868957.10.22489/CinC.2016.341-244Search in Google Scholar

[15] Qassim HM, Eesee AK, Osman OT, Jarjees MS. Controlling a motorized electric wheelchair based on face tilting. Bio-Algorithms Med-Systems 2019;15:1–7. DOI: 10.1515/bams-2019-0033.10.1515/bams-2019-0033Search in Google Scholar

[16] Roy J, Roy T, Mandal N, Postolache O. A Simple technique for heart sound detection and identification using kalman filter in real time analysis. 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI). Shanghai, China: IEEE, 2018:1-8. Available from: https://ieeexplore.ieee.org/abstract/document/8538255.10.1109/ISSI.2018.8538255Search in Google Scholar

[17] Bajelani K, Navidbakhsh M, Behnam H, Doyle JD, Hassani K. Detection and identification of first and second heart sounds using empirical mode decomposition. Proc Inst Mech Eng Pt H J Eng Med 2013;227:976–87.10.1177/0954411913493734Search in Google Scholar PubMed

[18] Deperliğlu Ö. Classification of segmented heart sounds with Artificial Neural Networks. Int J Appl Math Elec 2018;6:44–3910.18100/ijamec.2018447313Search in Google Scholar

[19] Tsao Y, Lin TH, Chen F, Chang YF, Cheng CH, Tsai KH. Robust S1 and S2 heart sound recognition based on spectral restoration and multi-style training. Biomed Signal Process Control 2019;49:173–80.10.1016/j.bspc.2018.10.014Search in Google Scholar

[20] Gomes EF, Bentley PJ, Coimbra M, Pereira E, Deng Y. Classifying heart sounds: approaches and results for the PASCAL challenge. In: Proc. 6th International Conference on Health Informatics, HealthInf 2013, Barcelona, Spain, 2013.Search in Google Scholar

Received: 2019-11-01
Accepted: 2019-12-31
Published Online: 2020-03-05

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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