An optimized framework for epileptic seizure detection using DWT-based feature extraction and hybrid dimensionality reduction

Rabel Guharoy; Nanda Dulal Jana; Suparna Biswas; Lalit Garg; Subhayu Ghosh

doi:10.2478/ijssis-2025-0060

.blurhash-client-img { display: none !important; }

An optimized framework for epileptic seizure detection using DWT-based feature extraction and hybrid dimensionality reduction

International Journal on Smart Sensing and Intelligent Systems

Volume 18 (2025): Issue 1 (January 2025)

By: Rabel Guharoy, Nanda Dulal Jana, Suparna Biswas, Lalit Garg and Subhayu Ghosh

Open Access

|Dec 2025

Figures & Tables

Block diagram of the proposed method. DWT, discrete wavelet transform; ICA, independent component analysis; KNN, K-nearest neighbor; LDA, linear discriminant analysis; NB, Naive Bayes; PCA, principal component analysis; SVM, support vector machine.

Block diagram of fifth level decomposition of the EEG signal. EEG, electroencephalogram.

Table 1:

Samples of data in normal and seizure cases

Set name	Annotation of data	Size (KB)	Acquisition circumstances
Set A	Z000.txt—Z100.txt	564	Five healthy subjects with open eyes
Set B	O000.txt—O100.txt	611	Five healthy subjects with closed eyes
Set C	N000.txt—N100.txt	560	Five people with epilepsy with seizure-free status
Set D	F000.txt—F100.txt	569	Five people with epilepsy with seizure-free status inside five epileptogenic zones
Set E	S000.txt—S100.txt	747	Five subjects during seizure activity

Table 2:

Performance metrics for ICA with KNN

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	88.50	93.99	81.14	85.24	93.99	0.89
A–D	83.50	82.65	83.87	83.99	82.65	0.82
A–E	93.00	100.00	86.01	88.36	100.00	0.94
B–C	91.50	93.33	89.83	90.47	93.33	0.92
B–D	91.50	93.31	90.37	90.12	93.31	0.91
B–E	92.00	100.00	84.07	86.32	100.00	0.92

[i] ICA, independent component analysis; KNN, K-nearest neighbor.

Table 3:

Performance metrics for ICA with SVM

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	88.00	92.68	83.67	86.33	92.68	0.89
A–D	85.50	96.03	75.96	79.14	96.03	0.86
A–E	97.50	100.00	94.96	95.55	100.00	0.98
B–C	86.50	83.63	89.65	91.07	83.63	0.87
B–D	90.50	89.63	92.59	91.72	89.63	0.90
B–E	94.50	100.00	88.78	90.69	100.00	0.95

[i] ICA, independent component analysis; SVM, support vector machine.

Table 4:

Performance metrics for ICA with NB

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	72.00	82.65	61.52	67.89	82.65	0.74
A–D	72.50	97.03	47.76	65.55	97.03	0.78
A–E	100.00	100.00	100.00	100.00	100.00	1.00
B–C	82.00	67.22	95.48	95.60	67.22	0.78
B–D	68.00	91.43	45.27	62.48	91.42	0.74
B–E	99.50	99.23	100.00	100.00	99.23	0.99

[i] ICA, independent component analysis; NB, Naive Bayes.

Table 5:

Results of PCA with KNN algorithm

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	81.00	88.47	71.87	78.36	88.47	0.83
A–D	90.50	93.20	89.28	89.34	93.20	0.91
A–E	58.00	100.00	18.18	57.18	100.00	0.71
B–C	81.00	82.32	77.93	79.39	82.32	0.81
B–D	83.50	83.09	81.94	83.12	83.09	0.89
B–E	88.50	100.00	75.07	86.68	100.00	0.92

[i] KNN, K-nearest neighbors; PCA, principal component analysis.

Table 6:

Results of PCA with SVM algorithm

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	77.50	96.87	55.57	71.09	96.87	0.81
A–D	84.50	97.07	71.43	77.77	97.07	0.86
A–E	93.50	100.00	86.87	89.34	100.00	0.94
B–C	83.00	93.65	71.89	77.89	93.65	0.85
B–D	85.00	93.85	74.36	80.27	93.85	0.86
B–E	90.00	100.00	80.09	83.98	100.00	0.91

[i] PCA, principal component analysis; SVM, support vector machine.

Table 7:

Results of PCA with NB algorithm

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	80.50	94.45	67.12	74.17	94.45	0.83
A–D	80.00	96.26	63.67	72.64	96.26	0.82
A–E	100.00	100.00	100.00	100.00	100.00	1.00
B–C	90.50	84.28	95.71	97.50	84.28	0.90
B–D	89.00	90.31	88.38	87.95	90.31	0.89
B–E	99.50	99.00	100.00	100.00	99.00	0.99

[i] NB, Naive Bayes; PCA, principal component analysis.

Table 8:

Results of proposed model LDA with KNN algorithm

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	77.50	82.95	70.30	76.89	82.95	0.78
A–D	66.50	64.45	67.84	69.40	64.45	0.66
A–E	92.00	100.00	84.65	86.49	100.00	0.92
B–C	76.50	64.08	87.32	82.89	64.08	0.72
B–D	80.00	73.44	82.98	85.54	73.44	0.77
B–E	90.00	100.00	79.61	84.92	100.00	0.91

[i] KNN, K-nearest neighbors; LDA, linear discriminant analysis.

Table 9:

Results of proposed model LDA with SVM algorithm

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	100.00	100.00	100.00	100.00	100.00	1.00
A–D	72.00	72.48	73.36	72.75	72.48	0.71
A–E	96.00	99.09	90.63	95.56	99.09	0.97
B–C	91.00	86.70	94.02	93.57	86.70	0.90
B–D	100.00	100.00	100.00	100.00	100.00	1.00
B–E	76.00	88.38	63.88	74.31	88.38	0.80

[i] LDA, linear discriminant analysis; SVM, support vector machine.

Table 10:

Results of proposed model LDA with NB algorithm

Case	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Recall (%)	F-measure
A–C	100.00	100.00	100.00	100.00	100.00	1.00
A–D	100.00	100.00	100.00	100.00	100.00	1.00
A–E	100.00	100.00	100.00	100.00	100.00	1.00
B–C	100.00	100.00	100.00	100.00	100.00	1.00
B–D	100.00	100.00	100.00	100.00	100.00	1.00
B–E	100.00	100.00	100.00	100.00	100.00	1.00

[i] LDA, linear discriminant analysis; NB, Naive Bayes.

Confusion matrix for PCA with KNN algorithm. KNN, K-nearest neighbors; PCA, principal component analysis.

Confusion matrix for PCA with SVM algorithm. PCA, principal component analysis; SVM, support vector machine.

Confusion matrix for PCA with NB algorithm. NB, Naive Bayes; PCA, principal component analysis.

Fold-wise accuracy using ICA and SVM, NB, KNN. ICA, independent component analysis; KNN, K-nearest neighbors; NB, Naive Bayes; SVM, support vector machine.

Fold-wise accuracy using PCA and SVM, NB, KNN. KNN, K-nearest neighbors; NB, Naive Bayes; PCA, principal component analysis; SVM, support vector machine.

Fold-wise accuracy using LDA and SVM, NB, KNN. KNN, K-nearest neighbors; LDA, linear discriminant analysis; NB, Naive Bayes; SVM, support vector machine.

ROC plot for PCA, ICA, LDA and SVM, NB, KNN. ICA, independent component analysis; KNN, K-nearest neighbors; LDA, linear discriminant analysis; NB, Naive Bayes; PCA, principal component analysis; SVM, support vector machine.

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/ijssis-2025-0060 | Journal eISSN: 1178-5608

Journal RSS Feed

Language: English

Submitted on: Jul 15, 2025

Published on: Dec 31, 2025

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

discrete wavelet transform,

electroencephalogram,

epilepsy,

independent component analysis,

linear discriminant analysis,

principal component analysis

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2025 Rabel Guharoy, Nanda Dulal Jana, Suparna Biswas, Lalit Garg, Subhayu Ghosh, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 18 (2025): Issue 1 (January 2025)

An optimized framework for epileptic seizure detection using DWT-based feature extraction and hybrid dimensionality reduction

Figures & Tables

Figure 1:

Figure 2:

Figure 3:

Table 1:

Table 2:

Table 3:

Table 4:

Table 5:

Table 6:

Table 7:

Table 8:

Table 9:

Table 10:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Paradigm

My account