New approaches to epileptic seizure prediction based on EEG signals using hybrid CNNs
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Dosyalar
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inderscience Enterprises Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study employs the University of Bonn Dataset to address the importance of frequency information in EEG data and introduces a methodology utilising the short-time Fourier transform. The proposed method transforms conventional 1D EEG signals into informative 2D spectrograms, offering an approach for advancing the detection of neurological diseases. Integrating advanced CNN architectures with the conversion of EEG signals into 2D spectrograms forms the foundation of our proposed methodology. The 1D CNN model utilised in this study demonstrates exceptional performance metrics, achieving a specificity of 0.996, an overall test accuracy of 0.991, a sensitivity of 0.987, and an F1 score of 0.989. Shifting to the 2D approach discloses a slight reduction in accuracy to 0.987, sensitivity of 0.976, specificity of 0.988, and an F1 score of 0.97. This analysis provides nuanced insights into the performance of 1D and 2D CNNs, clarifying respective strengths in the context of neurological disease detection.
Açıklama
Anahtar Kelimeler
Seizure Prediction, Epilepsy, EEG Signals, 1D Convolutional Neural Network, Deep Learning, Classification
Kaynak
International Journal of Intelligent Engineering Informatics
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
12
Sayı
1
