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In the evolving landscape of medical diagnostics, the ability to swiftly and accurately detect critical brain activities from electroencephalography (EEG) signals is paramount. This research introduces a novel algorithm. Designed to automate the detection and classification of harmful brain patterns such as seizures, which are crucial for timely medical intervention. Manual EEG analysis is resource-intensive and prone to inaccuracies, underscoring the urgent need for automated solutions. Our proposed method offers a streamlined, precise approach, empowering neurologists and researchers to identify seizure-related brain activities more efficiently, ultimately improving diagnostic accuracy and patient care.

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Automated EEG Analysis for Harmful Brain Activity Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Shalini Gambhir,
  • Disha Gusain,
  • Khushboo Chopra,
  • Anmol Gulati

摘要

In the evolving landscape of medical diagnostics, the ability to swiftly and accurately detect critical brain activities from electroencephalography (EEG) signals is paramount. This research introduces a novel algorithm. Designed to automate the detection and classification of harmful brain patterns such as seizures, which are crucial for timely medical intervention. Manual EEG analysis is resource-intensive and prone to inaccuracies, underscoring the urgent need for automated solutions. Our proposed method offers a streamlined, precise approach, empowering neurologists and researchers to identify seizure-related brain activities more efficiently, ultimately improving diagnostic accuracy and patient care.