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A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification.

Overview

Authors: Frnaz Akbar1, Yazeed Alkhrijah2, Syed Muhammad Usman3, Shehzad Khalid4,5, Imran Ihsan1, Mohamad A Alawad2
  1. Department of Creative Technologies, Faculty of Computing and AI, Air University, Islamabad, 44000 Pakistan
  2. Department of Electrical Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11623 Riyadh, Saudi Arabia
  3. Department of Computer Science, Bahria School of Engineering and Applied Sciences (BSEAS), Bahria University, Islamabad, 44000 Pakistan
  4. Department of Computer Engineering, Bahria School of Engineering and Applied Sciences (BSEAS), Bahria University, Islamabad, 44000 Pakistan
  5. Computer and Information Sciences Research Center (CISRC), Imam Mohammad Ibn Saud Islamic University (IMSIU), 11623 Riyadh, Saudi Arabia
Institutions: Air University (Pakistan); Imam Mohammad ibn Saud Islamic University (Saudi Arabia); Bahria University (Pakistan)
Journal: Scientific reports, volume 16, issue 1, article 13001
Dates: received 22 August 2025; accepted 4 March 2026; published online 11 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-43431-w · PMID 41814083 · PMCID PMC13099961 · OpenAlex W7135036895
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism), Alzheimer's / dementia (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Complexity, Physiology & signal measures
Keywords: Biomarkers, Computational biology and bioinformatics, Neurology, Neuroscience
MeSH: Alzheimer Disease*, Electroencephalography*, Support Vector Machine*, Classification Algorithms, Convolutional Neural Networks, Humans, Principal Component Analysis (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic University (IMSIU-DDRSP2601)
Citations: cited by 1 paper (Europe PMC); 49 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Tracing map

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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-43431-w.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 7 MeSH terms, 1 funder, 34 references.

Cite

This paper

Akbar, F., Alkhrijah, Y., Usman, S. M., Khalid, S., Ihsan, I., & Alawad, M. A. (2026). A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification. Scientific reports, 16(1), 13001. https://doi.org/10.1038/s41598-026-43431-w

BibTeX

@article{akbar2026deep,
author = {Akbar, Frnaz and Alkhrijah, Yazeed and Usman, Syed Muhammad and Khalid, Shehzad and Ihsan, Imran and Alawad, Mohamad A},
title = {{A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {13001},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-43431-w},
url = {https://doi.org/10.1038/s41598-026-43431-w},
pmid = {41814083},
pmcid = {PMC13099961}
}

RIS

TY - JOUR
AU - Akbar, Frnaz
AU - Alkhrijah, Yazeed
AU - Usman, Syed Muhammad
AU - Khalid, Shehzad
AU - Ihsan, Imran
AU - Alawad, Mohamad A
TI - A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/11
VL - 16
IS - 1
SP - 13001
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-43431-w
UR - https://doi.org/10.1038/s41598-026-43431-w
LA - en
ER -

CSL-JSON

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"volume": "16",
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"PMID": "41814083",
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"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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}

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