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Medical Imaging
EEG Cls

EEG Cls

Upload EEG data to classify signals as Normal or Abnormal

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What is EEG Cls ?

EEG Cls is an AI-powered medical imaging tool designed to classify EEG (electroencephalogram) signals as Normal or Abnormal. It simplifies the analysis process for clinicians and researchers by providing accurate and rapid classifications, enabling faster decision-making in neurological diagnostics.

Features

• Advanced Signal Processing: Utilizes cutting-edge AI algorithms to analyze EEG data with high accuracy.
• User-Friendly Interface: Easy to navigate, making it accessible for both specialists and non-specialists.
• Real-Time Analysis: Provides quick results, reducing the time needed for manual interpretation.
• Compatibility with Standard EEG Formats: Supports common file formats like EDIF, BDF, and CSV.

How to use EEG Cls ?

  1. Upload EEG Data: Load the EEG file into the tool through its intuitive interface.
  2. Select Parameters: Choose the specific settings or filters if required.
  3. Run Analysis: Initiate the classification process with the click of a button.
  4. Review Results: View the classification output, which will be labeled as Normal or Abnormal.
  5. Export Results: Save or download the results for further review or integration into patient records.

Frequently Asked Questions

What types of EEG files does EEG Cls support?
EEG Cls supports standard EEG file formats, including EDIF, BDF, and CSV. Ensure your data is in one of these formats for accurate analysis.

How accurate is EEG Cls in classifying signals?
EEG Cls uses advanced AI algorithms trained on large datasets, achieving high accuracy in classifying EEG signals. However, it is recommended to use it as a diagnostic aid and not a replacement for professional medical judgment.

Can EEG Cls be used by non-specialists?
Yes, EEG Cls is designed with a user-friendly interface, making it accessible for non-specialists. However, interpretation of results should be done under the guidance of a qualified healthcare professional.

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