Topics:
The scope of this workshop encompasses a wide range of topics related to the non-invasive assessment of neurological disorders. Participants are encouraged to contribute original research, review papers, and case studies that relate to these issues. This workshop is to provide an opportunity for information sharing, cooperation, and the creation of novel solutions to enhance the areas of medicine, pharmacy, biology, and biomedical engineering. Specific themes to be addressed include:
- Biomedical Signal Processing
- Neurological Disorders
- Machine Learning
- Neurodiagnostic Techniques
- Electroencephalography
- Neuroimaging Techniques
- Biomarker Identification
- Clinical Applications
Prospective authors are kindly invited to submit full papers that include title,
abstract, introduction, tables, figures, conclusion and references. It is
unnecessary to submit an abstract in advance. Please submit your papers in English.
Each paper should be no less than 4 pages. One regular registration can cover a
paper of 6 pages, and additional pages will be charged. Please format your paper
well according to the conference template before submission. Paper Template Download
Please prepare your paper in both .doc/.docx and .pdf format and
submit your full paper by email with both formats attached directly to ws_kayseri@icmmgh.org
Publication:
Workshop Proceedings
Accepted papers will be published in Theoretical and Natural Science (TNS)
(Print ISSN 2753-8818), and will be submitted to Conference Proceedings Citation
Index (CPCI), Crossref, CNKI, Portico, Engineering Village (Inspec), Google Scholar,
and other databases for indexing. The situation may be affected by factors among databases
like processing time, workflow, policy, etc.
Title: Theoretical and Natural Science (TNS)
Press: EWA Publishing, United Kingdom
ISSN: 2753-8818, 2753-8826 (electronic)
* The papers will be exported to production and publication on a regular basis.
Early-registered papers are expected to be published online earlier.
Highlights:
Neurological disorders (ND), such as Alzheimer’s disease, schizophrenia, depression, and mild cognitive impairment (MCI), significantly impact quality of life and pose diagnostic challenges due to overlapping symptoms and subjective evaluation methods. This workshop explores how biomedical analysis combined with machine learning can enhance ND assessment, focusing on electroencephalography (EEG) as a non-invasive, cost-effective diagnostic tool.
EEG data from participants, including individuals with neurological disorders (ND) and healthy controls, were analyzed using preprocessing, feature extraction, and selection methods informed by statistical and computational approaches. Various machine learning models, such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), were employed to classify the data. The models showed potential for both binary and multi-class classification tasks, highlighting the applicability of automated systems in supporting clinical decision-making processes.
The workshop highlights the critical role of frontal lobe EEG channels and specific biomarkers in ND diagnosis. Attendees will gain insights into EEG signal processing, feature optimization, and machine learning techniques tailored for ND assessment, with discussions on current challenges and future directions, such as expanding datasets and integrating deep learning approaches.
This session is ideal for professionals and researchers interested in advancing diagnostic tools and fostering interdisciplinary collaborations in biomedical engineering and healthcare.
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