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Iterative multiblock framework for high frequency EEG based neurological disorder detection
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  • Published: 22 January 2026

Iterative multiblock framework for high frequency EEG based neurological disorder detection

  • Rahul Agrawal1,
  • Chetan Dhule2,
  • Garima Shukla3,
  • Sofia Singh4,
  • Urvashi Agrawal5,
  • Sarah Allabun6,
  • Manal Othman6 &
  • …
  • Lotta Bayisenge7 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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  • Energy science and technology
  • Engineering
  • Health care

Abstract

It has become pertinent to develop early and accurate diagnosis tools for these neurological diseases, such as Alzheimer’s and Parkinson’s. The diagnosis may be high frequency electroencephalogram (EEG) signal based. These techniques promise good results but fail to obtain the desired clinically relevant features because of the intrinsically non-stationary and noisy nature of high frequency EEG components. Limitations of existing methods include suboptimal signal processing, ineffective strategies for feature selection, lack of robustness in feature fusion mechanisms, and limited explainability for clinical adoptions. This work, therefore, proposes a holistic framework in the context of clinical detection of neurological disorders using high frequency EEG signals which are enhanced as a pipeline of multi-blocks. The combination of Hilbert-Huang transform (HHT) with a modified empirical mode decomposition ensures that the decomposition is adaptive in nature and effective noise reduction leads to preprocessing of the data. Wavelet Packets transform (WPT) in conjunction with shannon entropy-based feature selection reduces the dimensions of the data without information loss, which aids in meaningful extraction of temporal and frequency domain features. Canonical correlation analysis with multi-view representation learning allows integration of EEG features along with clinical metadata as auxiliary information to create a common feature space for increased sensitivity in diagnosis. A new multi-scale convolutional recurrent neural network (MS-CRNN) uses an attention mechanism to process the combined features and find spatiotemporal dependencies while focusing on patterns that are important for diagnosis. The method is demonstrated through grad-cam and integrated gradient techniques that help in visualizing and quantitatively attributing feature extraction. This method was 94% accurate; 92% sensitive; and 93% specific when identifying issues early on. The high accuracy in making clinical interpretation and diagnosis has set a new bar for clinicians and has encouraged public policy to support early intervention.

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Data availability

This paper has used two datasets for experiments: Temple University Hospital (TUH) EEG Corpus [31] and the CHB-MIT Scalp EEG Database [32].

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Acknowledgements

This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R393), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R393), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

  1. Department of Data Science, IoT, Cybersecurity (DIC), G H Raisoni College of Engineering, Nagpur, Maharashtra, India

    Rahul Agrawal

  2. Department of Data Science, IoT, Cybersecurity (DIC), G H Raisoni College of Engineering, Nagpur, Maharashtra, India

    Chetan Dhule

  3. Department of Computer Science and Engineering, Amity School of Engineering & Technology, Amity University, Mumbai, India

    Garima Shukla

  4. Department of AI, Amity School of Engineering & Technology, Amity University, Noida, India

    Sofia Singh

  5. Department of Electronics & Telecommunication Engineering, Jhulelal Institute of Technology, Nagpur, India

    Urvashi Agrawal

  6. Department of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia

    Sarah Allabun & Manal Othman

  7. Department of Health Sciences, College of Medicine and Health Sciences, University of Rwanda, PO BOX 4285, Kigali, Rwanda

    Lotta Bayisenge

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Contributions

Data analysis and code writing: RA with the help of CD, GS, SS. Data acquisition and preprocessing: SS, UA and SA. Writing, original draft preparation, RA and CD. Writing, review and editing: MO, LB. Funding acquisition: MO. Project administration: SA. All authors read and approved the final manuscript.

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Correspondence to Rahul Agrawal or Lotta Bayisenge.

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Agrawal, R., Dhule, C., Shukla, G. et al. Iterative multiblock framework for high frequency EEG based neurological disorder detection. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37126-5

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  • Received: 20 June 2025

  • Accepted: 20 January 2026

  • Published: 22 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-37126-5

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Keywords

  • Neurological disorder detection
  • High frequency EEG
  • Multi-scale CRNN
  • Hilbert-Huang transform
  • Explainable AI
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