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

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Aydin S., Saraoglu H. M., Kara S.

ANNALS OF BIOMEDICAL ENGINEERING, cilt.37, ss.2626-2630, 2009 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1007/s10439-009-9795-x
  • Dergi Adı: ANNALS OF BIOMEDICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.2626-2630
  • Anahtar Kelimeler: EEG classification, Log Energy Entropy, Neural network, Seizure, APPROXIMATE ENTROPY
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

https://link.springer.com/article/10.1007%2Fs10439-009-9795-x

https://pubmed.ncbi.nlm.nih.gov/19757057/

 

ABSTRTACT

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.

 

DISCUSSION AND CONCLUSION

n this study, three datasets consisting of normal and epileptic records in addition to ictal series are classified by using several MLNN architectures. The inputs of the NNs, i.e., the signal features, are computed by addressing the ShanEn, LogEn, and SamEn.

The results show that epileptic records (Set-D and Set-E) show lower entropies in comparison to healthy records (Set-A). In particular, seizure activity produces significantly lower entropies. It means that electrophysiological behavior of epileptogenic regions is less complex than behavior of healthy brain. It can be said that lower entropy indicates the severity of epilepsy. Since the LogEn values meet the most reliable features to analyze the nonlinear dynamics of both cortical and intracortical neuronal interactions, we propose the LogEn values as inputs of the MLNN in discriminating the seizure.

 

Entropy is a useful quantity to recognize the certain features of EEG series such that small number of dominating process provides the low entropy. Results show that the LogEn is highly sensitive to the degree of EEG complexity. Since, seizure is a synchronous neuronal activity producing spike discharges, the LogEn can be used for seizure detection. The LogEn can also be proposed to critical analysis of EEG in many diagnostic applications such as discriminating of sleep disorders and monitoring of anesthetic depth.