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August 14, 2025Applied SciencesOpen Access

Multilayer Neural-Network-Based EEG Analysis for the Detection of Epilepsy, Migraine, and Schizophrenia

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Authors

IDIbrahim H. DursunMAMehmet AkınMAMehmet Ufuk Aluçlu

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Overview

This multiclass machine learning system detects epilepsy, migraine, and schizophrenia, implying advanced EEG signal classification.

Key Points

  • Achieving 95% sensitivity and 96% specificity highlights the model's diagnostic accuracy for neurological disorders.
  • The multilayer neural network processes EEG signals for classifying epilepsy, migraine, and schizophrenia simultaneously.
  • Utilizing feature extraction from frequency subbands enhances performance in distinguishing these conditions.
  • This approach supports scalable decision-making tools in clinical settings, promoting real-time diagnostic applications.

Cite This Study

Dursun et al. (2025) studied this question.

synapsesocial.com/papers/68af509bad7bf08b1ead88fdhttps://doi.org/10.3390/app15168983
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases2024
  2. 2Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning2025
  3. 3Discrete mathematical modelling for enhancing mental illness detection2025
  4. 4An Enhanced Machine Learning–Based Multimodal Framework for Seizure Detection Using EEG and MRI Data2026
  5. 5Automatic and Efficient Framework for Identifying Multiple Neurological Disorders From EEG Signals2023 · 79 citations