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Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure

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Abstract

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Faculty of Engineering, The University of Ondokuz Mayıs, Kurupelit, 55139, Samsun, Turkey

    Serap Aydın

  2. Faculty of Engineering, The University of Dumlupınar, Kutahya, Turkey

    Hamdi Melih Saraoğlu

  3. Biomedical Engineering Institute, The University of Fatih, Istanbul, Turkey

    Sadık Kara

Authors
  1. Serap Aydın
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  2. Hamdi Melih Saraoğlu
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  3. Sadık Kara
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Correspondence to Serap Aydın.

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Aydın, S., Saraoğlu, H.M. & Kara, S. Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure. Ann Biomed Eng 37, 2626–2630 (2009). https://doi.org/10.1007/s10439-009-9795-x

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  • DOI: https://doi.org/10.1007/s10439-009-9795-x

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