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REVIEW 4 major objections 6 minor 1 cited by

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A hybrid wavelet + 1D-CNN + multi-head attention model classifies one-second EEG windows as seizure or non-seizure with 99.83% accuracy, beating all compared models on a benchmark dataset.

desk verdict The 99.83% SOTA claim collapses under a subject-wise split; the reported metrics contradict the confusion matrix, so this incremental CNN+attention paper is not ready for review. read the letter →

arxiv 2501.10342 v1 pith:4S4CSF4Y submitted 2025-01-17 cs.LG

Mohammed Guhdar , Ramadhan J. Mstafa , Abdulhakeem O. Mohammed This is my paper · ORCID
classification cs.LG
keywords epilepticseizuredetectionEEGsignalclassification1Dconvolutionalneuralnetworkmulti-headattentionwavelettransformdeeplearningbinary
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Epilepsy affects roughly 50 million people, and automatic seizure detection from EEG is hard because the signals are noisy, non-stationary, and vary across people. The paper proposes a hybrid model—single-level wavelet denoising followed by three 1D convolutional layers and a four-head attention layer with a skip connection—that classifies one-second EEG windows as seizure or non-seizure. On the benchmark dataset of 11,500 such windows, the authors report 99.83% test accuracy, an MCC of 0.9950, and an F1 score of 0.9990. Their claim is that this combination outperforms all compared models on the same dataset, including LSTM and Bayesian hybrids.

What carries the argument

The mechanism that carries the argument is a three-stage pipeline: a single-level wavelet transform using the db1 wavelet smooths noise while preserving high and low frequencies; three 1D convolutional blocks (32, 64, and 128 filters with kernel sizes 7, 5, and 3, each followed by batch normalization and max pooling) extract local temporal patterns; and a multi-head attention layer with four heads and key dimension 32 lets the model weigh different time positions simultaneously, with a skip connection adding the last convolutional output to the attention output. Global average pooling, two dense layers with dropout of 0.5, L2 regularization at 0.001, early stopping, and learning-rate reduction are used to control overfitting. The final sigmoid neuron maps the representation to a seizure versus non-seizure decision.

What would settle it

Run the identical pipeline but split by subject: train only on segments from a subset of the 500 subjects and test only on held-out subjects' segments, then compare the resulting accuracy with the reported 99.83% random-split result.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that attention, not recurrence, is the better complement to a convolutional stack for EEG seizure classification. The input is a 178-point one-second window; the first three layers grow filter counts 32 to 64 to 128 while shrinking kernel sizes 7 to 5 to 3, and the final convolutional output is joined by a skip connection to a four-head attention layer with key dimension 32. The authors report that on the 2,300 test windows the model makes four errors—one false positive and three false negatives—which gives the 99.83% figure. They present this as the highest accuracy on the benchmark compared with the prior models in their comparison table.

Load-bearing premise

The load-bearing premise is that the 23 one-second segments taken from the same person's recording are independent enough that a random split into training and test sets gives an honest measure of accuracy.

Editorial extensions

If this is right

  • One second of a single EEG channel is enough for the seizure/non-seizure decision, so the model could run in low-latency monitoring settings that flag abnormal windows as they arrive.
  • The attention weights provide a per-window importance map, giving a clinician a view of which moments within a segment drove the decision rather than a bare binary label.
  • On the paper's comparison table, the model would rank above the 99.47% Bayesian Conv1D-LSTM and the 99.3% Conv1D-LSTM, the two closest prior results.
  • Because the design uses no recurrent layer, training and inference avoid the sequential bottlenecks of LSTM-based hybrids.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A subject-wise split is the natural extension: each of the 500 subjects contributes 23 contiguous one-second windows, so a random window split may let the model learn subject-specific baselines; a held-out-subject test would likely produce a lower accuracy than the reported 99.83%.
  • The attention heat-map suggests the model could double as a localizer: the per-time-step weights might indicate when within a segment epileptiform activity begins, a use the paper does not develop.
  • The same wavelet + 1D-CNN + multi-head attention recipe transfers directly to other one-dimensional biomedical time series, such as ECG arrhythmia or EMG event detection, where local waveform shape and long-range timing both matter.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes a hybrid deep learning model for binary epileptic seizure detection from EEG, combining a single-level Daubechies-1 wavelet transform, three 1D convolutional layers with batch normalization and max pooling, a four-head attention mechanism with a skip connection, global average pooling, and two dense layers with dropout and L2 regularization. The authors evaluate on the UCI Epileptic Seizure Dataset, binarizing the five original classes into seizure versus non-seizure, and report 99.83% test accuracy, 0.995 MCC, 0.999 F1, and 0.999 CSI, claiming to outperform all existing models on this dataset. The paper includes a confusion matrix, accuracy/loss curves, and a comparison table of prior results.

Significance. If the reported performance were obtained under a subject-independent evaluation protocol, the architecture would be a useful reference point for EEG-based seizure detection, and the systematic description of regularization and attention integration is a strength. However, the current evidence does not support the central claim: the random segment-level split creates likely subject leakage, and the reported F1/CSI values are inconsistent with the confusion matrix. The paper provides no code, random seed, or data split details, which limits reproducibility. The work is therefore best viewed as an architecture proposal requiring re-validation.

major comments (4)
  1. [§3.1.1 and §5.1] The dataset construction in Section 3.1.1 creates 23 contiguous one-second segments from each subject's single 23.6-second recording, yielding 11,500 samples, and Section 5.1 describes only an 80/20 split of the dataset without any subject-wise separation. Because segments from the same subject are highly correlated and non-independent, a random split over all segments is very likely to place segments from the same subject in both training and test sets, allowing the model to memorize subject-specific characteristics and inflate the reported 99.83% accuracy. The central state-of-the-art claim in the Abstract and Table 2 is therefore not established. The authors must re-evaluate with a subject-wise (group-wise) split, such as splitting by subject IDs or leave-one-subject-out, and report the resulting performance.
  2. [§5.1, Table 1 and Figure 10] Table 1 reports F1 = 0.9990 and CSI = 0.9990, but the confusion matrix in Figure 10 (TP=462, FP=1, FN=3, TN=1834) yields F1 = 2·462/(2·462+1+3) = 0.9957 and CSI = 462/(462+1+3) = 0.9914, while the test accuracy of 99.83% (2296/2300) is consistent with the matrix. This inconsistency means at least one of the reported results is incorrect and undermines confidence in the reliability of the reported metrics. The authors should recompute and present a consistent set of metrics.
  3. [§5.2, Table 2] Table 2 compares the proposed model with prior methods reporting accuracies on the same dataset, but the manuscript does not establish that the cited results were obtained under the same evaluation protocol (e.g., random segment split versus subject-wise split, cross-validation scheme, preprocessing steps such as wavelet transform and feature scaling). Without this information, the claim that the proposed model outperforms all existing models is not justified; differences in accuracy may reflect differences in evaluation protocol rather than model quality.
  4. [§4.2 and §5.1] The reproducibility of the reported results is limited: Section 5.1 gives no random seed, no stratification details, and no code or implementation link, and Section 4.2 says only that a single-level db1 wavelet transform is applied without specifying whether the approximation coefficients, detail coefficients, or a reconstructed signal are used as the (178,1) input to the network. These missing details prevent a reader from reproducing the 99.83% accuracy result.
minor comments (6)
  1. [References] References [8] and [10] are identical, and references [29] and [30] are identical; duplicate citations should be merged or renumbered.
  2. [Tables 1 and 2] The headers in Table 1 and Table 2 contain spacing artifacts, "V alue" and "Publication Y ear", which should be corrected to "Value" and "Publication Year".
  3. [§4.2] The statement that the wavelet transform "retains both high- and low-frequency data" is not enough to determine the model input; please specify how the wavelet coefficients are processed and how the resulting representation matches the declared input shape (178,1).
  4. [§5.1] The phrase "miss-classifications" should be "misclassifications", and in the caption of Figure 2, "preporcessing" should be "preprocessing".
  5. [Figure 8] Figure 8 is described as a "heat-map for weighting process", but the figure as printed does not show an interpretable heat-map; please replace it with a clear visualization of the attention weights or remove it.
  6. [§3.1.1] The sentence "with total of 178 + 1 (y-output) column" should use the plural "columns" and clarify that each of the 11,500 rows is a segment with 178 features and one label.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation is present; the accuracy claim is empirical, while the subject-leakage and metric-consistency concerns are validity issues rather than circular reductions.

full rationale

This is an empirical architecture-comparison paper, not a derivation, so the classical circularity modes do not arise. The claimed 99.83% test accuracy is a measured value on a 20% held-out segment split, not a quantity defined from the training targets: the target labels are never used as input features, and the model is not fitted to the test labels. The comparison in Table 2 is against prior published numbers on the same public UCI dataset, with no reliance on the authors' own prior theorems or ansatz. There are no self-citations that carry a load-bearing argument; all cited baselines, metrics, and dataset references are external. The paper itself warns in the Introduction that apparent gains 'may occasionally be attributed to overfitting rather than genuine generalization,' which is directly relevant to the reviewer concern about a segment-level 80/20 split: Section 3.1.1 creates 23 contiguous one-second windows per subject, and Section 5.1 does not state a subject-wise split, so segments from the same 23.6-second recording can appear in both training and test sets. That is a genuine validity and leakage risk for the accuracy claim, and Table 1 metrics are not arithmetically consistent with the Figure 10 confusion matrix (F1 and CSI would be roughly 0.9957 and 0.9914, not 0.9990). Those are support and robustness problems, not circularity: the 99.83% figure is not derived from its own inputs by construction and is not forced by a self-citation chain. Hence no circular step is identified, and the circularity score is 0.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a dataset split that likely leaks subject identity between training and test, and on a set of hand-picked hyperparameters without a proper tuning or validation protocol. The standard metrics are misreported, and no code or detailed experimental configuration is provided.

free parameters (8)
  • Conv1D filter counts = [32, 64, 128]
    Chosen by hand in Section 4.1; no systematic search or separate validation described.
  • Conv1D kernel sizes = [7, 5, 3]
    Chosen by hand in Section 4.1; no ablation or tuning shown.
  • Number of attention heads = 4
    Chosen by hand in Section 4.1; no sensitivity analysis.
  • Attention key dimension = 32
    Chosen by hand in Section 4.1; no justification given.
  • Dropout rate = 0.5
    Chosen by hand in Section 4.4; no tuning reported.
  • L2 regularization factor = 0.001
    Chosen by hand in Section 4.1; no tuning reported.
  • Train/test split ratio and random seed = 80/20, seed unspecified
    Section 5.1; no seed means the single reported result is not reproducible, and the split may not be stratified by subject.
  • Wavelet type and level = db1, single level
    Chosen in Section 4.2; no comparison with other wavelets or levels.
assumptions (5)
  • domain assumption The 23 segments extracted from each subject can be treated as independent samples.
    Section 3.1.1 divides each subject's recording into 23 one-second segments; Section 5.1 applies an 80/20 split over all 11,500 samples without subject-wise separation. This is the load-bearing assumption that makes the test set valid, and it is likely false because contiguous segments from the same recording are highly correlated.
  • domain assumption Merging classes 2 through 5 into one non-seizure class is appropriate for the detection task.
    Section 3.1.2 re-labels the five original classes into a binary problem. The validity of treating tumor-region and healthy-brain recordings as a single negative class is assumed without discussion.
  • domain assumption The UCI Epileptic Seizure Dataset is representative enough to support claims about clinical seizure detection.
    The dataset is used throughout as the only evaluation benchmark; no external validation on other datasets is performed.
  • domain assumption StandardScaler and single-level db1 wavelet preserve the information needed for accurate classification.
    Sections 3.2 and 4.2 state these preprocessing choices but provide no comparison to alternative scalers or wavelets, and no evidence that clinically relevant seizure features are retained.
  • standard math Standard evaluation metrics (accuracy, precision, recall, F1, CSI, MCC) are computed correctly.
    Equations (2)-(7) define standard metrics, but as applied in Table 1 they are inconsistent with the confusion matrix, so this axiom is violated in practice.

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Cite this review

Pith. "Pith review of Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism." pith.science (2026). https://pith.science/paper/4S4CSF4Y

@misc{pith2026250110342,
  author       = {Pith},
  title        = {Pith review of: Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4S4CSF4Y}},
  note         = {Machine review of arXiv:2501.10342}
}
read the original abstract

Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity and frequency, which results in loss of consciousness and muscle contractions for a short period of time \cite{epilepsyfoundation_myoclonic}. Individuals with epilepsy often face significant employment challenges due to safety concerns in certain work environments. Many jobs that involve working at heights, operating heavy machinery, or in other potentially hazardous settings may be restricted for people with seizure disorders. This certainly limits job options and economic opportunities for those living with epilepsy.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks

    cs.LG 2026-06 unverdicted novelty 2.0 of 10

    A review arguing that Kolmogorov-Arnold Networks address key limitations of deep learning models for EEG-based seizure detection through improved interpretability and efficiency.

Reference graph

Works this paper leans on

41 extracted references · 39 canonical work pages · cited by 1 Pith paper

  1. [26]

    Computational and Mathematical Methods in Medicine 2022(1), 7751263 (2022)

    Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: [retracted] review on epileptic seizure prediction: Machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022(1), 7751263 (2022)

    work page 2022

  2. [1]

    Accessed: September 9, 2024

    Organization, W.H.: Epilepsy. Accessed: September 9, 2024. https://www.who. int/news-room/fact-sheets/detail/epilepsy

    work page 2024

  3. [2]

    Accessed: September 9, 2024

    Foundation, E.: Myoclonic Seizures. Accessed: September 9, 2024. https://www. epilepsy.com/what-is-epilepsy/seizure-types/myoclonic-seizures

    work page 2024

  4. [3]

    Journal of Clinical Neurology (Seoul, Korea) 17(3), 393 (2021)

    Jeon, J.-Y., Lee, H., Shin, J.-Y., Moon, H.-J., Lee, S.-Y., Kim, J.-M., et al.: Increasing trends in the incidence and prevalence of epilepsy in korea. Journal of Clinical Neurology (Seoul, Korea) 17(3), 393 (2021)

    work page 2021

  5. [4]

    In: Artificial Intelligence in Medicine: 17th Con- ference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings 17, pp

    Roy, S., Kiral-Kornek, I., Harrer, S.: Chrononet: A deep recurrent neural network for abnormal eeg identification. In: Artificial Intelligence in Medicine: 17th Con- ference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings 17, pp. 47–56 (2019). Springer

    work page 2019

  6. [5]

    Neurocomputing 414, 90–100 (2020)

    Zhu, A., Wu, Q., Cui, R., Wang, T., Hang, W., Hua, G., Snoussi, H.: Explor- ing a rich spatial–temporal dependent relational model for skeleton-based action recognition by bidirectional lstm-cnn. Neurocomputing 414, 90–100 (2020)

    work page 2020

  7. [6]

    Advances in neural information processing systems 32 (2019) 17

    Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32 (2019) 17

    work page 2019

  8. [7]

    Archives of Computational Methods in Engineering 31(4), 2345–2384 (2024)

    Ranjan, R., Sahana, B.C., Bhandari, A.K.: Deep learning models for diagnosis of schizophrenia using eeg signals: emerging trends, challenges, and prospects. Archives of Computational Methods in Engineering 31(4), 2345–2384 (2024)

    work page 2024

Show all 41 references
  1. [9]

    Neural Computing and Applications, 1–26 (2024)

    Badr, Y., Tariq, U., Al-Shargie, F., Babiloni, F., Al Mughairbi, F., Al-Nashash, H.: A review on evaluating mental stress by deep learning using eeg signals. Neural Computing and Applications, 1–26 (2024)

    2024

  2. [10]

    Multimedia Tools and Applications, 1–23 (2024)

    Dutta, A.K., Raparthi, M., Alsaadi, M., Bhatt, M.W., Dodda, S.B., Sandhu, M., Patni, J.C.: Deep learning-based multi-head self-attention model for human epilepsy identification from eeg signal for biomedical traits. Multimedia Tools and Applications, 1–23 (2024)

    2024

  3. [11]

    Rupapara, V., Rustam, F., Ishaq, A., Lee, E., Ashraf, I.: Chi-square and pca based feature selection for diabetes detection with ensemble classifier. Intell. Autom. Soft Comput 36(2), 1931–1949 (2023)

    2023

  4. [12]

    Waves in Random and Complex Media, 1–27 (2023)

    Hussain, L., Alabdulkreem, E., Lone, K.J., Al-Wesabi, F.N., Nour, M.K., Hilal, A.M., Marzouk, R., Aziz, S.: Feature ranking chi-square method to improve the epileptic seizure prediction by employing machine learning algorithms. Waves in Random and Complex Media, 1–27 (2023)

    2023

  5. [13]

    Expert Systems with Applications 219, 119527 (2023)

    Chaudhari, K., Thakkar, A.: Neural network systems with an integrated coef- ficient of variation-based feature selection for stock price and trend prediction. Expert Systems with Applications 219, 119527 (2023)

    2023

  6. [14]

    Applied Soft Computing 133, 109924 (2023)

    Amorim, L.B., Cavalcanti, G.D., Cruz, R.M.: The choice of scaling technique matters for classification performance. Applied Soft Computing 133, 109924 (2023)

    2023

  7. [15]

    Frontiers in Human Neuroscience 18, 1319574 (2024)

    Blanco-R ´ ıos, M.A., Candela-Leal, M.O., Orozco-Romo, C., Remis-Serna, P., V´ elez-Saboy´ a, C.S., Lozoya-Santos, J.d.J., Cebral-Loureda, M., Ram ´ ırez-Moreno, M.A.: Real-time eeg-based emotion recognition for neurohumanities: perspectives from principal component analysis a...

    2024

  8. [16]

    IEEE Transactions on signal processing 44(9), 2163–2171 (1996)

    Erdol, N., Basbug, F.: Wavelet transform based adaptive filters: analysis and new results. IEEE Transactions on signal processing 44(9), 2163–2171 (1996)

    1996

  9. [17]

    Signal Processing 214, 109258 (2024) 18

    Ruiz, J., Schlotthauer, G., Vignolo, L., Colominas, M.A.: Fully adaptive time- varying wave-shape model: Applications in biomedical signal processing. Signal Processing 214, 109258 (2024) 18

    2024

  10. [18]

    Computers, Materials and Continua (2020)

    Zhao, Y., Cheng, J., Zhang, P., Peng, X.: Ecg classification using deep cnn improved by wavelet transform. Computers, Materials and Continua (2020)

    2020

  11. [19]

    circulation 101(23), 215–220 (2000)

    Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: Physiobank, phys- iotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation 101(23), ...

    2000

  12. [20]

    Biomedical signal processing and control 72, 103342 (2022)

    Li, H., Ding, M., Zhang, R., Xiu, C.: Motor imagery eeg classification algo- rithm based on cnn-lstm feature fusion network. Biomedical signal processing and control 72, 103342 (2022)

    2022

  13. [21]

    Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology 16, 1–6 (2008)

    Brunner, C., Leeb, R., M¨ uller-Putz, G., Schl¨ ogl, A., Pfurtscheller, G.: Bci com- petition 2008–graz data set a. Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology 16, 1–6 (2008)

    2008

  14. [22]

    Signal, Image and Video Processing 18(2), 1577–1588 (2024)

    Sadam, S.S.P., Nalini, N.: Epileptic seizure detection using scalogram-based hybrid cnn model on eeg signals. Signal, Image and Video Processing 18(2), 1577–1588 (2024)

    2024

  15. [24]

    Physical Review E 64(6), 061907 (2001)

    Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E 64(6), 061907 (2001)

    2001

  16. [25]

    In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp

    Torse, D.A., Khanai, R.: Classification of epileptic seizures using ensemble empir- ical mode decomposition and least squares support vector machine. In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5 (2021). IEEE

    2021

  17. [27]

    In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp

    George, F., Joseph, A., Baby, B., John, A., John, T., Deepak, M., Nithin, G., Sathidevi, P.: Epileptic seizure prediction using eeg images. In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1595–1598 (2020). IEEE

    2020

  18. [28]

    Biology 11(8), 1220 (2022) 19

    Hilal, A.M., Albraikan, A.A., Dhahbi, S., Nour, M.K., Mohamed, A., Motwakel, A., Zamani, A.S., Rizwanullah, M.: Intelligent epileptic seizure detection and classification model using optimal deep canonical sparse autoencoder. Biology 11(8), 1220 (2022) 19

    2022

  19. [30]

    Knowledge-Based Systems 265, 110372 (2023)

    Liu, S., Wang, Z., An, Y., Zhao, J., Zhao, Y., Zhang, Y.-D.: Eeg emotion recog- nition based on the attention mechanism and pre-trained convolution capsule network. Knowledge-Based Systems 265, 110372 (2023)

    2023

  20. [31]

    Pattern Recognition Letters 128, 544–550 (2019)

    Thara, D., PremaSudha, B., Xiong, F.: Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques. Pattern Recognition Letters 128, 544–550 (2019)

    2019

  21. [32]

    Powers, D.: Evaluation: From precision, recall and f-factor to roc, informedness, markedness & correlation (tech. rep.). Adelaide, Australia (2007)

    2007

  22. [33]

    arXiv preprint arXiv:2010.16061 (2020)

    Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informed- ness, markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)

    2020 arXiv

  23. [34]

    Epilepsia 64(6), 1466–1468 (2023)

    Mbizvo, G.K., Bennett, K.H., Simpson, C.R., Duncan, S.E., Chin, R.F., Larner, A.J.: Using critical success index or gilbert skill score as composite measures of positive predictive value and sensitivity in diagnostic accuracy studies: Weather forecasting informing epilepsy res...

    2023

  24. [35]

    BioData Mining 16(1), 4 (2023)

    Chicco, D., Jurman, G.: The matthews correlation coefficient (mcc) should replace the roc auc as the standard metric for assessing binary classification. BioData Mining 16(1), 4 (2023)

    2023

  25. [36]

    Journal of Engineering and Applied Science 71(1), 21 (2024)

    Kunekar, P., Gupta, M.K., Gaur, P.: Detection of epileptic seizure in eeg signals using machine learning and deep learning techniques. Journal of Engineering and Applied Science 71(1), 21 (2024)

    2024

  26. [37]

    Neural Computing and Applications 36(6), 2835–2852 (2024)

    Omar, A., Abd El-Hafeez, T.: Optimizing epileptic seizure recognition per- formance with feature scaling and dropout layers. Neural Computing and Applications 36(6), 2835–2852 (2024)

    2024

  27. [38]

    Brain Informatics 11(1), 21 (2024)

    Bhadra, R., Singh, P.K., Mahmud, M.: Hyepiseid: a hybrid convolutional neu- ral network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals. Brain Informatics 11(1), 21 (2024)

    2024

  28. [39]

    Computers In Biology And Medicine 148, 105931 (2022)

    Abenna, S., Nahid, M., Bouyghf, H., Ouacha, B.: Eeg-based bci: A novel improve- ment for eeg signals classification based on real-time preprocessing. Computers In Biology And Medicine 148, 105931 (2022)

    2022

  29. [40]

    Informatics in Medicine Unlocked21, 100444 (2020)

    Almustafa, K.M.: Classification of epileptic seizure dataset using different machine learning algorithms. Informatics in Medicine Unlocked21, 100444 (2020)

    2020

  30. [41]

    Engineering Proceedings 59(1), 166 (2024)

    Kunekar, P., Kumawat, C., Lande, V., Lokhande, S., Mandhana, R., Kshirsagar, 20 M.: Comparison of different machine learning algorithms to classify epilepsy seizure from eeg signals. Engineering Proceedings 59(1), 166 (2024)

    2024

  31. [42]

    Journal of Investigations on Engineering and Technology 4(2), 47–60 (2021)

    Nahzat, S., Ya˘ gano˘ glu, M.: Classification of epileptic seizure dataset using differ- ent machine learning algorithms and pca feature reduction technique. Journal of Investigations on Engineering and Technology 4(2), 47–60 (2021)

    2021

  32. [43]

    In: 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp

    Jain, M., Bhardwaj, H., Srivastav, A.: Bayesian-enhanced eeg signal analysis for epileptic seizure recognition: A 1d-cnn lstm approach. In: 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 1–6 ...

    2024

  33. [44]

    Multimedia Tools and Applications 83(8), 22119–22151 (2024) 21

    Gupta, D., Gupta, U., Sarma, H.J.: Functional iterative approach for universum- based primal twin bounded support vector machine to eeg classification (fuptb- svm). Multimedia Tools and Applications 83(8), 22119–22151 (2024) 21

    2024

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