Synopsis

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting millions of people worldwide. Early and accurate detection of seizures is crucial for timely medical intervention and improving the quality of life for patients. Electroencephalography (EEG) is the most common diagnostic tool for monitoring brain activity and detecting seizures. However, manual analysis of EEG signals by neurologists is time-consuming and prone to human error, especially in long-term monitoring scenarios. We aim to develop a deep learning-based system for automated seizure detection using EEG signals. The model will leverage advanced architectures like Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), or hybrid models to analyze temporal and spectral features of EEG data. This approach will enable real-time seizure detection, reduce false alarms, and assist clinicians in making informed decisions.

Relevance of the Topic

Seizure detection from EEG signals is a critical task in epilepsy diagnosis and management. Automated systems can provide continuous monitoring, reduce the burden on healthcare professionals, and improve patient outcomes by enabling faster intervention. With the increasing availability of large EEG datasets and advancements in deep learning, there is significant potential to develop robust models that can accurately detect seizures and differentiate them from normal brain activity or artifacts.

Future Research/Scope

• Real-time Monitoring: Optimize the model for real-time seizure detection to integrate it into wearable devices or clinical monitoring systems.
• Multi-modal Data Integration: Combine EEG with other physiological signals (e.g., ECG, EMG) to improve detection accuracy and provide a more comprehensive understanding of seizure activity.
• Personalized Models: Develop patient-specific models that adapt to individual EEG patterns, improving detection accuracy for personalized healthcare.
• Explainability: Incorporate explainability techniques to highlight key EEG features contributing to seizure detection, ensuring transparency for clinicians.
• Long-term Monitoring: Extend the model to analyze long-term EEG recordings, enabling the detection of rare or subtle seizure patterns.

Skills Learned

• Deep Learning: Hands-on experience with RNNs, CNNs, and hybrid models for time-series and signal-processing tasks.
• Signal Processing: Understanding of EEG signal preprocessing, including filtering, denoising, and feature extraction (e.g., Fourier Transform, Wavelet Transform).
• Python Programming: Proficiency in Python and libraries like TensorFlow, PyTorch, and SciPy for implementing deep learning models.
• Data Visualization: Skills in visualizing EEG signals and model predictions using tools like Matplotlib and Plotly.

Relevant courses to the topic

• CSE422: Artificial Intelligence
• CSE425: Neural Networks
• CSE427: Machine Learning
• CSE443: Bioinformatics I
• CSE430: Digital Signal Processing
• Coursera: AI for Medical Diagnosis

Reading List/Study Materials

• Books: 
  • "Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction" – Andrea Varsavsky, Iven Mareels, Mark Cook
    Link
• Research Papers: 
  • "EEG-based epileptic seizure detection using deep learning techniques: A survey" – Neurocomputing
    Link: Neurocomputing
  • "Epileptic Seizure Detection Based on EEG Signals and CNN" – Frontiers in Neuroinformatics
    Link: Frontiers in Neuroinformatics
  • "EEG-Based Epileptic Seizure Detection Using Binary Dragonfly Algorithm and Deep Neural Network" – Nature Scientific Reports
    Link: Nature Scientific Reports
  • "EEG Seizure Detection: Concepts, Techniques, Challenges, and Future Directions" – Multimedia Tools and Applications
    Link: PubMed Central

• Datasets
  • CHB-MIT Scalp EEG Database
    Link
  • Bonn University EEG Dataset
    Link | Kaggle Preprocessed
  • SWEC-ETHZ iEEG Database and Algorithms
    Link
  • TUH EEG Corpus (Temple University Hospital EEG Corpus)
    Link
  • EPILEPSIAE (European Epilepsy Database)
    Link
  • UPenn and Mayo Clinic's Seizure Detection Challenge
    Link
  • Kaggle: American Epilepsy Society Seizure Prediction Challenge
    Link

• Tutorials and Guides
  • Epilepsy Tutorial – Brainstorm
    It guides users through EEG analysis for epilepsy detection using Brainstorm software.
    Link: Brainstorm
• Code Tutorials
  • Seizure-Detection-Tutorials – GitHub Repository by Eldave93
    Python-based tutorials for EEG seizure detection using open-source datasets.
    Link: GitHub
  • HCTSA Time-series Classification using the Bonn University EEG dataset
    Link: GitHub
• Videos
  • Understanding EEG: A Practical Guide for Patients and Families – YouTube
    Explains EEG, its importance in epilepsy diagnosis, and what to expect during an EEG procedure.
    Link: YouTube