Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier

GONG Chen; LIU Jiahui; NIU Yunyun

doi:10.1049/cje.2021.03.005

Volume 30 Issue 3

May 2021

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GONG Chen, LIU Jiahui, NIU Yunyun, “Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 419-425, 2021, doi: 10.1049/cje.2021.03.005

Citation:

GONG Chen, LIU Jiahui, NIU Yunyun, “Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 419-425, 2021, doi: 10.1049/cje.2021.03.005

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Intracranial Epileptic Seizures Detection Based on an Optimized Neural Network Classifier

doi: 10.1049/cje.2021.03.005

GONG Chen^1,2,3,
LIU Jiahui¹,
NIU Yunyun^{1
,
,}

1. School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China;
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.61872325), the Fundamental Research Funds for the Central Universities (No.2652019028), and the China Scholarship Council (No.201806405004).

Received Date: 2020-09-13

Abstract

Abstract

Automatic identification of intracranial electroencephalogram (iEEG) signals has become more and more important in the field of medical diagnostics. In this paper, an optimized neural network classifier is proposed based on an improved feature extraction method for the identification of iEEG epileptic seizures. Four kinds of entropy, Sample entropy, Approximate entropy, Shannon entropy, Log energy entropy are extracted from the database as the feature vectors of Neural network (NN) during the identification process. Four kinds of classification tasks, namely Pre-ictal v Post-ictal (CD), Pre-ictal v Epileptic (CE), Post-ictal v Epileptic (DE), Pre-ictal v Post-ictal v Epileptic (CDE), are used to test the effect of our classification method. The experimental results show that our algorithm achieves higher performance in all tasks than previous algorithms. The effect of hidden layer nodes number is investigated by a constructive approach named growth method. We obtain the optimized number ranges of hidden layer nodes for the binary classification problems CD, CE, DE, and the multitask classification problem CDE, respectively.
- iEEG,
- Neural network (NN),
- Entropy,
- Feature extraction,
- Mutual range of coefficient,
- Hidden layer node

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References(32)

References

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