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Effectual seizure detection using MBBF-GPSO with CNN network.

Dinesh Kumar Atal1, Mukhtiar Singh1

  • 1Department of Electrical Engineering, Delhi Technological University, Bawana Road, Delhi, 110042 India.

Cognitive Neurodynamics
|June 3, 2024
PubMed
Summary
This summary is machine-generated.

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Seizures: Classification01:13

Seizures: Classification

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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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Optimal Deep CNN-Based Vectorial Variation Filter for Medical Image Denoising.

Journal of digital imagingยท2023
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This study introduces a novel framework for automatic seizure detection using a modified Blackman bandpass filter-greedy particle swarm optimization (MBBF-GPSO) and a convolutional neural network (CNN). This approach enhances accuracy and efficiency in identifying seizures from EEG data.

Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Signal Processing

Background:

  • Electroencephalography (EEG) is crucial for seizure diagnosis, reflecting brain's electrical activity.
  • Conventional automatic seizure detection methods face challenges in feature selection, computational complexity, and accuracy.
  • There is a need for advanced frameworks to improve seizure detection performance.

Purpose of the Study:

  • To propose a novel framework, MBBF-GPSO with CNN, for effective and accurate automatic seizure detection.
  • To address limitations of existing methods by optimizing feature selection and reducing computational burden.
  • To enhance the efficacy of seizure detection using a hybrid approach.

Main Methods:

  • Utilized a modified Blackman bandpass filter (MBBF) to eliminate noise and improve stopband attenuation.
Keywords:
CNN-convolutional neural networkGPSO-greedy particle swarm optimizationMBBF-modified Blackman bandpass filter

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  • Employed greedy particle swarm optimization (GPSO) with time and frequency domain features for optimized feature selection.
  • Integrated convolutional neural network (CNN) for productive classification and automatic feature learning.

    • Main Results:

    • The MBBF-GPSO-CNN framework demonstrated superior performance in seizure detection.
    • Optimized feature selection via MBBF-GPSO enhanced the efficiency of the detection process.
    • CNN effectively learned distinct features for accurate seizure classification.

    Conclusions:

    • The proposed MBBF-GPSO-CNN framework offers a practical and effective solution for automatic seizure detection.
    • This hybrid approach overcomes limitations of conventional methods, providing higher accuracy and efficiency.
    • Further validation through performance and comparative analysis confirms the system's advantages.