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MFCC-CNN: A patient-independent seizure prediction model

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Abstract

Background

Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications.

Methods

This paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model.

Results

Experimental results showed that the proposed model obtained accuracy of 96\(\%\), sensitivity of 92\(\%\), specificity of 84\(\%\) and F1-score of 85\(\%\) for 24 cases in CNHB-MIT dataset. The overall performance of MFCC-CNN model is better than the other models.

Conclusion

MFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability.

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Author information

Authors and Affiliations

  1. Radiology department, Huaihe Hospital of Henan University, Kaifeng, 475004, China

    Fan Zhang

  2. School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China

    Fan Zhang & Siyuan Guo

  3. School of Software, Henan University, Kaifeng, 475004, China

    Boyan Zhang & Xinhong Zhang

Authors
  1. Fan Zhang
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  2. Boyan Zhang
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  3. Siyuan Guo
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  4. Xinhong Zhang
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Contributions

Fan Zhang: Conceptualization, Methodology, Formal analysis, Software, Writing-original draft preparation, Writing-review and editing. Boyan Zhang: Formal analysis, Validation, Writing-original draft preparation. Siyuan Guo: Methodology, Software, Validation. Xinhong Zhang: Conceptualization, Methodology, Writing-review and editing.

Corresponding author

Correspondence to Xinhong Zhang.

Ethics declarations

Ethics approval

Approval by Ethic Committee was not required as the data set used in this retrospective study came from a publicly accessible database.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Informed consent

was obtained from all individual participants included in the study.

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Cite this article

Zhang, F., Zhang, B., Guo, S. et al. MFCC-CNN: A patient-independent seizure prediction model. Neurol Sci (2024). https://doi.org/10.1007/s10072-024-07718-y

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