NEURAL-NETWORK SEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS FOR EPILEPTIFORM ACTIVITY DETECTION

Authors

  • Svetlana Bezobrazova
  • Vladimir Golovko

DOI:

https://doi.org/10.47839/ijc.7.3.521

Keywords:

Artificial Neural Networks, Electroencephalogram Analysis, Epilepsy Detection, Lyapunov’s Exponent, Signal Segmentation

Abstract

A goal of EEG signals analysis is not only human psychologically and functionality states definition but also pathological activity detection. In this paper we present an approach for epileptiform activity detection by artificial neural network technique for EEG signal segmentation and for the highest Lyapunov’s exponent computing. The EEG segmentation by the neural network approach makes it possible to detect an abnormal activity in signals. We examine our system for segmentation and anomaly detection on the EEG signals where the anomaly is an epileptiform activity.

References

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Published

2014-08-01

How to Cite

Bezobrazova, S., & Golovko, V. (2014). NEURAL-NETWORK SEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS FOR EPILEPTIFORM ACTIVITY DETECTION. International Journal of Computing, 7(3), 30-37. https://doi.org/10.47839/ijc.7.3.521

Issue

2008, Volume 7, Issue 3

Section

Articles

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