Skip to main content

Advertisement

SpringerLink
Log in

Backpropagation Artificial Neural Network Detects Changes in Electro-Encephalogram Power Spectra of Syncopic Patients

  • Original Article
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This paper presents an effective application of backpropagation artificial neural network (ANN) in differentiating electroencephalogram (EEG) power spectra of syncopic and normal subjects. Digitized 8-channel EEG data were recorded with standard electrodes placement and amplifier settings from five confirmed syncopic and five normal subjects. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation and analysis of changes due to syncope. The results revealed significant increase in percentage δ and α (p<0.5 or better) with significant reduction in percentage θ activity (p<0.05). The backpropagation ANN used for classification contains 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from syncopic EEG power spectra and the normal EEG power spectra with an accuracy of 88.87% (85.75% for syncopic and 92% for normal).

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Lamarre-Cliché, M., and Cusson, J., The fainting patient: value of the head-upright tilt table test in adult patients with orthostatic intolerancem. Can. Med. Assoc. J. 164:372–376, 2001.

    Google Scholar 

  2. Udani, V., Bavdekar, M., and Karia, S., Head up tilt test in the diagnosis of neurocardiogenic syncope in childhood and adolescence. Neurol. India 52:185–187, 2004.

    Google Scholar 

  3. Yadav, P., Sethi, A., Agarwal, A. K., and Jain, A., Syncope. J. Indian Acad. Clin. Med. 4:286–291, 2003.

    Google Scholar 

  4. Sheldon, R., Sexton, E., and Koshman, M. L. R., Usefulness of clinical factors in predicting outcomes of passive tilt test in patients with syncope. Am. J. Cardiol. 85:360–364, 2000.

    Article  Google Scholar 

  5. Maurice, V., and Ropper, A. H., Principles of Neurology, 7th edn., McGraw Hill, USA, 2001.

    Google Scholar 

  6. Sherman, D. L., Brambrink, A. M., Ichord, R. N., Dasika, V., Koehler, R. C., Hanley, D. F., Koehler, R. C., Traystman, R. J., and Thakor, N. V., The EEG during early recovery from hypoxic-ischemic injury in immature piglets: the role of normalized separation and bursting. Clin. Electroencephalogr. 30:175–183, 1999.

    Google Scholar 

  7. Bezerianos, A., Tong, S., and Thakor, N. V., Tsallis entropy estimation of EEG rhythm following ischemia. Ann. Biomed. Eng. 31:221–232, 2003.

    Article  Google Scholar 

  8. Barlow, M., and Leitch, J., How to treat syncope. Aust. Doct. 33–40, 2004.

  9. Goel, V., Brambrink, A. M., Baykal, A., Koehler, R. C., Hanle, D. F., and Thakor, N. V., Dominant frequency analysis of EEG reveals brain's response during injury and recovery. IEEE Trans. Biomed. Eng. 43:1083–1092, 1996.

    Article  Google Scholar 

  10. Williams, G. W., Luders, H. O., Brickner, A., Goormastic, M., and Klass, D. W., Interobserver variability in EEG interpretation. Neurology 35:1714–1719, 1985.

    Google Scholar 

  11. Sinha, R. K., Artificial neural network detects changes in electro-encephalogram power spectrum of different sleep-wake states in an animal model of heat stress. Med. Biol. Eng. Comput. 41:595–600, 2003.

    Article  Google Scholar 

  12. Dubois, M., Sato, S., Lees, D. E., Bull, J. M., Smith, R., White, B. G., Moore, H., and Macnamara T. E., Electroencephalographic changes during whole body hyperthermia in humans. Electroencephalogr. Clin. Neurophysiol. 50:486–495, 1980.

    Article  Google Scholar 

  13. Sinha, R. K., Electro-encephalogram disturbances in different sleep-wake states following exposure to high environmental heat. Medical and Biological Eng. Comput. 42:282–287, 2004.

    Article  Google Scholar 

  14. Jervis, B., Coelho, M., and Morgan, G. W., Spectral analysis of EEG responses. Med. Biol. Eng. Comput. 27:230–238, 1989.

    Article  Google Scholar 

  15. Rao, V., and Rao, H., C++ Neural Networks and Fuzzy Logic, 1st edn., BPB Publications, New Delhi, 1996.

    Google Scholar 

  16. Zurada, J. M., Introduction to Artificial Neural Network Systems, West Publishing Company, St. Paul, MN, 1997.

    Google Scholar 

  17. Hassoun, H. M., Fundamentals of Artificial Neural Networks, Printice-Hall of India Private Limited, New Delhi, 1998.

    Google Scholar 

  18. Rakotomamonjy, A., Migeon, B., and Marche, P., Automated neural network detection of wavelet preprocessed electrocardiogram late potentials. Med. Biol. Eng. Comput. 36:346–350, 1998.

    Article  Google Scholar 

  19. Muthuswamy, J., Kimura, T., Ding, M. C., Geocadin, R., Hanley, D. F., and Thakor, N. V., Vulnerability of the thalamic somatosensory pathway after prolonged global hypoxic-ischemic injury. Neuresuscitationience 115:917–929, 2002.

    Google Scholar 

  20. Al-Nashash, H. A. M., A dynamic Fourier series for the compression of ECG using FFT and adaptive coefficient estimate. Med. Eng. Phys. 17:197–203, 1995.

    Article  Google Scholar 

  21. Sarbadhikari, S. N., Dey, S., and Ray, A. K., Chronic exercise alters EEG power spectra in an animal model of depression. Indian J. Physiol. Pharmacol. 40:47–57, 1996.

    Google Scholar 

  22. Kulkarni, P. K., Kumar, V., and Verma, H. K., Diagnostic acceptability of FFT-based ECG data compression. J. Med. Eng. Technol. 21:185–189, 1997.

    Article  Google Scholar 

  23. Sarbadhikari, S. N., A Neural network confirms that physical exercise reverses EEG changes in depressed rats. Med. Eng. Phys. 17:579–582, 1995.

    Article  Google Scholar 

  24. Kosinski, D. J., and Grubb, B. P., Neurally mediated syncope with an update on indications and usefulness of head-upright tilt table testing and pharmacologic therapy. Curr. Opin. Cardiol. 9:53–64, 1994.

    Article  Google Scholar 

  25. Edner, A., Katz-Salamon, M., Lagercrantz, H., and Milerad, J., Heart rate response profiles during head upright tilt test in infants with apparent life threatening events. Arch. Dis. Child. 76:27–30, 1997.

    Article  Google Scholar 

  26. Davis, T. L., and Freemon, F. R., Electroencephalography should not be routine in the evaluation of syncope in adult. Arch. Intern. Med. 150:2027–2029, 1990.

    Article  Google Scholar 

  27. Linzer, M., Yang, E. H., Estes, N. A. III, Wang, P., Vorperian, V. R., and Kapoor, W. N., Diagnosing syncope. Value of history, physical examination and electrocardiography, clinical efficacy, assessment projection of the American college of physicians. Ann. Intern. Med. 126:988–996, 1997.

    Google Scholar 

  28. Driscoll, D. J., Jacobsen, S. J., Porter, C. J., and Wollan, P. C., syncope in children and adolescents. J. Am. Coll. Cardiol. 29:1039–1045, 1997.

    Article  Google Scholar 

  29. Gomes, M. M., Kropf, L. A., Breeck, E. S., and Figueira, I. L., Interferences from a community study about non-epileptic events. Arq. de Neuro-Psiquiatr. 60:712–716, 2002.

    Google Scholar 

  30. Rowland, L. P., Merritts Neurology, 10th edn., Lippincott Williams & Wilkins, Philadelphia, 2000.

    Google Scholar 

  31. Nejad, V. S., Ashtari, F., and Khorvash, F., A study of the relationship between syncope attacks and diminished carotid and vertebral artery flow using Doppler ultrasonography of cervical vessels. J. Res. Med. Sci. 10:97–100, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Instrumentation, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India

    Rakesh Kumar Sinha & Barda Nand Das

  2. Department of Electrical & Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India

    Yogender Aggarwal

Authors
  1. Rakesh Kumar Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yogender Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Barda Nand Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rakesh Kumar Sinha.

Additional information

Certificate of Originality—This is to certify that the article submitted for publication in ‘Journal of Medical Systems’ has not been publ-ished, nor is being considered for publication, elsewhere. (Rakesh Kumar Sinha)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sinha, R.K., Aggarwal, Y. & Das, B.N. Backpropagation Artificial Neural Network Detects Changes in Electro-Encephalogram Power Spectra of Syncopic Patients. J Med Syst 31, 63–68 (2007). https://doi.org/10.1007/s10916-006-9043-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-006-9043-y

Keywords

Search

Navigation