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JPM
Special Issues
Personalized Treatment of Neurological Diseases
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Personalized Treatment of Neurological Diseases

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Personalized Therapy and Drug Delivery".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2211

Special Issue Editors

Dr. Theodora Afrantou

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Guest Editor
Second Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, Stilp. Kyriakidis 1, 54636 Thessaloniki, Greece
Interests: neurology; neurological diseases; epilepsy; dementia; Alzheimer’s disease
Dr. Panagiotis Ioannidis

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Guest Editor
Second Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, Stilp. Kyriakidis 1, 54636 Thessaloniki, Greece
Interests: neurology; neurological diseases; dementia; Alzheimer’s disease
Special Issues, Collections and Topics in MDPI journals
Dr. Katerina D. Tzimourta

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Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, GR50100 Kozani, Greece
Interests: biomedical signal processing; EEG signal processing; brain–computer interface; machine learning; EEG wearable devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of the latest advancements and challenges in the field of personalized treatment of neurological diseases. For the Special Issue, I would like to invite you to submit original research articles and review articles that focus on the individualized treatment of patients with neurological diseases, including epilepsy, dementia, and other related conditions.

Specifically, I would like to invite authors to submit articles that focus on the following topics:

Individualized treatment of patients with epilepsy: this can include a comprehensive review of all data in the literature around the individualized holistic approach of the patient in relation to comorbidities, age, sex, side effects of drugs, EEG, imaging, gene testing, and other relevant factors. Additionally, I would like to invite authors to submit articles on the individualized treatment of specific subcategories of epilepsy, such as autoimmune epilepsy.

Personalized treatment for patients with dementia: this can include articles that focus on the latest advancements in personalized treatment for patients with dementia, including pharmacological and non-pharmacological interventions.

Personalized approach for patients with dementia through artificial intelligence: this can include articles that focus on the application of quantitative EEG in the diagnosis of patients with dementia and how artificial intelligence can be used to personalize treatment approaches for patients with dementia.

I believe that this Special Issue will be of great interest to researchers, clinicians, and practitioners working in the field of neurology and neuroscience. I look forward to receiving your contributions and working with you to create a valuable resource for the scientific community.

Dr. Theodora Afrantou
Dr. Panagiotis Ioannidis
Dr. Katerina D. Tzimourta
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neurological diseases
  • neurodegenerative diseases
  • Alzheimer’s disease
  • Parkinson’s disease
  • neurodegeneration
  • dementia
  • epilepsy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

25 pages, 3206 KiB  
Article
A Novel CNN-Based Framework for Alzheimer’s Disease Detection Using EEG Spectrogram Representations
by Konstantinos Stefanou, Katerina D. Tzimourta, Christos Bellos, Georgios Stergios, Konstantinos Markoglou, Emmanouil Gionanidis, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas and Andreas Miltiadous
J. Pers. Med. 2025, 15(1), 27; https://doi.org/10.3390/jpm15010027 - 14 Jan 2025
Viewed by 1714
Abstract
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dementia (FTD), is slow and resource-intensive, underscoring the need for automated approaches. [...] Read more.
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dementia (FTD), is slow and resource-intensive, underscoring the need for automated approaches. Methods: To address this gap, this study proposes a novel deep learning methodology for EEG classification of AD, FTD, and control (CN) signals. The approach incorporates advanced preprocessing techniques and CNN classification of FFT-based spectrograms and is evaluated using the leave-N-subjects-out validation, ensuring robust cross-subject generalizability. Results: The results indicate that the proposed methodology outperforms state-of-the-art machine learning and EEG-specific neural network models, achieving an accuracy of 79.45% for AD/CN classification and 80.69% for AD+FTD/CN classification. Conclusions: These results highlight the potential of EEG-based deep learning models for early dementia screening, enabling more efficient, scalable, and accessible diagnostic tools. Full article
(This article belongs to the Special Issue Personalized Treatment of Neurological Diseases)
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