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Performance Analysis of ApEn as a Feature Extraction Technique and Time Delay Neural Networks, Multi Layer Perceptron as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals

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Computational Intelligence, Cyber Security and Computational Models

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 412))

Abstract

Epilepsy being a very common and chronic neurological disorder has a pathetic effect on the lives of human beings. The seizures in epilepsy are due to the unexpected and transient electrical disturbances in the cortical regions of the brain. Analysis of the Electroencephalography (EEG) Signals helps to understand the detection of epilepsy risk levels in a better perspective. This paper deals with the Approximate Entropy (ApEn) as a Feature Extraction Technique followed by the possible usage of Time Delay Neural Network (TDNN) and Multi Layer Perceptron (MLP) as post classifiers for the classification of epilepsy risk levels from EEG signals. The analysis is done in terms of bench mark parameters such as Performance Index (PI), Quality Values (QV), Sensitivity, Specificity, Time Delay and Accuracy.

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Authors and Affiliations

  1. Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India

    Sunil Kumar Prabhakar & Harikumar Rajaguru

Authors
  1. Sunil Kumar Prabhakar
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  2. Harikumar Rajaguru
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Corresponding author

Correspondence to Sunil Kumar Prabhakar .

Editor information

Editors and Affiliations

  1. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Muthukrishnan Senthilkumar

  2. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Vijayalakshmi Ramasamy

  3. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Shina Sheen

  4. Applied Mathematics and Computational Sc, PSG College of Technology, Coimbatore, Tamil Nadu, India

    C. Veeramani

  5. Department of Mathematics, Ryerson University, TORONTO, Ontario, Canada

    Anthony Bonato

  6. Fac. Science&Technology, Deakin University, Burwood, Australia

    Lynn Batten

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© 2016 Springer Science+Business Media Singapore

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Prabhakar, S.K., Rajaguru, H. (2016). Performance Analysis of ApEn as a Feature Extraction Technique and Time Delay Neural Networks, Multi Layer Perceptron as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG Signals. In: Senthilkumar, M., Ramasamy, V., Sheen, S., Veeramani, C., Bonato, A., Batten, L. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 412. Springer, Singapore. https://doi.org/10.1007/978-981-10-0251-9_10

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  • DOI: https://doi.org/10.1007/978-981-10-0251-9_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0250-2

  • Online ISBN: 978-981-10-0251-9

  • eBook Packages: EngineeringEngineering (R0)

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