Skip to main content
Log in

Neural Network Detector of ECG Signal Distortions

  • Published:
Biomedical Engineering Aims and scope

The use of artificial neural networks for detection of ECG signal distortions was discussed. Training and test databases were compiled. A technique for analysis of training samples based on the k-means clustering method was suggested. The effect of the number of hidden layer neurons on the neural network efficiency was studied. A method for testing the neural network efficiency based on the receiver operating characteristic (ROC) curve was developed. The structural principle of the neural network detector of ECG signal distortions was also developed. Testing of the system demonstrated high values of sensitivity and specificity (94.5%), as well as a high mean value of AUC (0.97).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. http://www.who.int/mediacentre/factsheets/fs317/ru/.

  2. Popechitelev E.P., Methods of Biomedical Research: Systemic Aspects [in Russian], ZhITI, Zhitomir (1997).

    Google Scholar 

  3. Gorodin A.S., ECG Signal Quality Control under Conditions of Telemedical Monitoring: Master’s Thesis [in Russian], Orel (2014).

  4. Maan A.C., van Zwet E.W., Man S., Oliveira-Martens S.M.M., Schalij M.J., Swenne C.A., Comput. Cardiol., 38, 289-292 (2011).

    Google Scholar 

  5. Kozyura A.V., In: Proc. X Int. Sci. Conf. Physics and Radioelectronis in Medicine and Ecology, Vol. 1, Vladimir (2012), pp. 152-155.

  6. Al-Haidri W.A., Isakov R.V., and Sushkova L.T., In: Proc. XI Int. Sci. Conf. Physics and Radioelectronis in Medicine and Ecology (2014), pp. 379-382.

  7. Josy Joy, Manimegalai P., J. Eng. Comput. Appl. Sci., 2, No. 8 (2013).

  8. Geetha G., Geethalakshmi S.N., Int. J. Eng. Sci. Tech., 3 (2011).

  9. Heenam Yoon, Hanbyul Kim, Sungjun Kwon, Kwangsuk Park, In: Proc. V Int. Conf. Health, Telemedicine, and Social Medicine, IARIA (2013).

  10. Al-Mabruk M., Hardware, Software, and Algorithms for Diagnosis of Heart Pathology Based on Perceptron Networks: Candidate’s Thesis [in Russian], Ryazan (2011).

  11. Galushka V.V., Fatkhi V.A., Inzh. Vestn. Dona, 25, No. 2 (2013).

  12. Baturkin S.A., Baturkina E.Yu., Zimenko V.A., Siginov I.V., Vestn. RGRTU, 31, No. 1 (2010).

  13. Cherezov S., Tyukachev N.A., Vestn. VGU Ser. Sist. Anal. Inform. Tekh., No. 2, 25-29 (2009).

  14. Al-Haidri W.A., Isakov R.V., and Sushkova L.T., Neirokomp. Razrab. Prim., No. 7, 60-66 (2015).

  15. Al-Huleidi W.A., Isakov R.V., and Sushkova L.T., Neirokomp. Razrab. Prim., No. 6, 61-67 (2012).

  16. Budchenko A.A., Mazurova I.Yu., Ilyukhin V.I., Khrapova N.P., In: Proc. VII Int. Sci. Conf. Systemic Analysis in Medicine, Blagoveshchensk (2013).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to W. A. Al-Haidri.

Additional information

Translated from Meditsinskaya Tekhnika, Vol. 50, No. 3, May-Jun., 2016, pp. 18-22.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Haidri, W.A., Isakov, R.V. & Sushkova, L.T. Neural Network Detector of ECG Signal Distortions. Biomed Eng 50, 170–174 (2016). https://doi.org/10.1007/s10527-016-9612-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10527-016-9612-4

Navigation