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Seizure Onset Detection by Analyzing Long-Duration EEG Signals

  • Garima Chandel
  • Omar Farooq
  • Yusuf U. Khan
  • Mayank Chawla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

Seizures in epileptic patients affect tremendously their daily life in terms of accidents during driving a vehicle, swimming, using stairs, etc. Automatic seizure detectors are used to detect seizure as early as possible so that an alarm can be given to patient or their family for using anti-epileptic drugs (AEDs). In this paper, an algorithm has been proposed for automatic seizure onset detection by analysis of electroencephalogram (EEG) signals. The method is based on few wavelet transform-based features and two statistical features without wavelet decomposition for improving the performance of detector. The mean, energy, and entropy were calculated on different wavelet decomposed subbands, and mean absolute deviation and interquartile range were calculated on raw signal. Classification between seizure and nonseizure types of EEG signals was done successfully by linear classifier. The algorithm was applied to CHB-MIT EEG dataset for seizure onset detection and achieved 100 % sensitivity with mean latency of 1.9 s.

Keywords

Feature Vector Wavelet Decomposition Epileptic Patient Seizure Onset Linear Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer India 2016

Authors and Affiliations

  • Garima Chandel
    • 1
  • Omar Farooq
    • 1
  • Yusuf U. Khan
    • 2
  • Mayank Chawla
    • 3
  1. 1.Department of Electronics EngineeringAligarh Muslim UniversityAligarhIndia
  2. 2.Department of Electrical EngineeringAligarh Muslim UniversityAligarhIndia
  3. 3.S-LabsHyderabadIndia

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