A Survey on Epilepsy Detection and Classifications Using Automated Approaches

  • Authors

    • Srinath R
    • Gayathri R
    https://doi.org/10.14419/ijet.v7i4.6.28679
  • Epilepsy, Features, Classifications, Deep learning, Machine learning.

  • Abstract

    This paper discusses various methods for the automatic detection and classification of focal and non-focal EEG signals for the detection of Epilepsy disease. The feature extraction and classification methods which were used in many conventional Epilepsy classification methods are discussed in this paper. The machine learning algorithms requires number of input features from the images for improving the classification rate. The deep learning algorithms do not require any extracted features from the source EEG signals. This classification algorithm takes the signal as input features and produces the classification result. The classification rates of these deep learning algorithms are high due to its stability with input sources.

     

  • References

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  • How to Cite

    R, S., & R, G. (2018). A Survey on Epilepsy Detection and Classifications Using Automated Approaches. International Journal of Engineering & Technology, 7(4.6), 529-531. https://doi.org/10.14419/ijet.v7i4.6.28679

    Received date: 2019-03-30

    Accepted date: 2019-03-30