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Integration of DWT, FFT, and Spatial domain for the identification of epileptic seizure utilizing electroencephalogram signal

Rabel Guharoy

Rabel Guharoy

  • IT Department, Rashtriya Raksha UniversityGandhinagar, India
  • CSE Department, Amity UniversityKolkata, India
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Guharoy, Rabel
, 
Nanda Dulal Jana

Nanda Dulal Jana

CSE Department, Amity UniversityKolkata, India
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Jana, Nanda Dulal
, 
Suparna Biswas

Suparna Biswas

ECE Department, Guru Nanak Institute of TechnologyKolkata, India
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Biswas, Suparna
 y 
Aveek Chattopadhyaya

Aveek Chattopadhyaya

EE Department, Guru Nanak Institute of TechnologyKolkata, India
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Chattopadhyaya, Aveek
  
08 ago 2025
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Categoría del artículo: Research Article
Publicado en línea: 08 ago 2025
Recibido: 06 mar 2025
DOI: https://doi.org/10.2478/ijssis-2025-0041
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© 2025 Rabel Guharoy et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Proposed model of epilepsy detection process. DWT, discrete wavelet transform; EEG, electroencephalogram; FFT, fast Fourier transform; LDA, linear discriminant analysis; PCA, principal component analysis.
Proposed model of epilepsy detection process. DWT, discrete wavelet transform; EEG, electroencephalogram; FFT, fast Fourier transform; LDA, linear discriminant analysis; PCA, principal component analysis.

Figure 2:

DWT decomposition up to level two (2). DWT, discrete wavelet transform; EEG, electroencephalogram.
DWT decomposition up to level two (2). DWT, discrete wavelet transform; EEG, electroencephalogram.

Figure 3:

Samples of EEG signals of 5 different classes. EEG, electroencephalogram.
Samples of EEG signals of 5 different classes. EEG, electroencephalogram.

Figure 4:

Samples of A1 after applying DWT of 5 different classes. DWT, discrete wavelet transform.
Samples of A1 after applying DWT of 5 different classes. DWT, discrete wavelet transform.

Figure 5:

Samples of EEG signal after application of FFT for 5 different classes. EEG, electroencephalogram; FFT, fast Fourier transform.
Samples of EEG signal after application of FFT for 5 different classes. EEG, electroencephalogram; FFT, fast Fourier transform.

Figure 6:

Bar chart of total number of samples non-seizure and seizure.
Bar chart of total number of samples non-seizure and seizure.

Performance measure of case 1 (combined features + PCA)

Algorithm Accuracy Precision Recall F-1 Score
LR 0.99 0.99 0.97 0.98
K-NN 0.98 0.97 0.96 0.97
SVM 0.98 0.97 0.96 0.97

Performance measure of case 2 (combined features + LDA)

Algorithm Accuracy Precision Recall F-1 Score
LR 0.97 0.96 0.92 0.94
K-NN 0.96 0.96 0.90 0.92
SVM 0.96 0.96 0.90 0.92

Performance measure of case 3 (combined features)

Algorithm Accuracy Precision Recall F-1 Score
LR 0.98 0.96 0.96 0.96
K-NN 0.95 0.96 0.89 0.92
SVM 0.95 0.96 0.89 0.92

Comparison with other techniques

References Classifier Accuracy (%)
[41] Decision tree 99
[42] Multilayer perceptron neural network 99
[43] ANN 98.30
[44] SVM 97.98
[45] PCA with RF 92.69
[45] PCA with ANN 97.55
Proposed model LR 99
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