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A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine

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DOI: 10.4236/jbise.2010.36078    7,529 Downloads   12,387 Views   Citations

ABSTRACT

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. Substantial data is generated by the EEG recordings of ambulatory recording systems, and detection of epileptic activity requires a time-consuming analysis of the complete length of the EEG time series data by a neurology expert. A variety of automatic epilepsy detection systems have been developed during the last ten years. In this paper, we investigate the potential of a recently-proposed statistical measure parameter regarded as Sample Entropy (SampEn), as a method of feature extraction to the task of classifying three different kinds of EEG signals (normal, interictal and ictal) and detecting epileptic seizures. It is known that the value of the SampEn falls suddenly during an epileptic seizure and this fact is utilized in the proposed diagnosis system. Two different kinds of classification models, back-propagation neural network (BPNN) and the recently-developed extreme learning machine (ELM) are tested in this study. Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Song, Y. and Liò, P. (2010) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. Journal of Biomedical Science and Engineering, 3, 556-567. doi: 10.4236/jbise.2010.36078.

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