New Feature Extraction Approach for Epileptic EEG Signal Detection using Time-Frequency Distributions Articles uri icon

authors

  • GUERRERO MOSQUERA, CARLOS ANDRES
  • MALANDA TRIGUEROS, ARMANDO
  • IRIARTE FRANCO, JORGE
  • NAVIA VAZQUEZ, ANGEL

publication date

  • April 2010

start page

  • 321

end page

  • 330

issue

  • 4

volume

  • 48

International Standard Serial Number (ISSN)

  • 0140-0118

Electronic International Standard Serial Number (EISSN)

  • 1741-0444

abstract

  • This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time&-frequency distributions (TFDs). Particularly, the method extracts
    features from the Smoothed Pseudo Wigner-Ville distribution using tracks
    estimated from the McAulay-Quatieri sinusoidal model. The proposed
    features are the length, frequency, and energy of the principal track.
    We evaluate the proposed scheme using several datasets and we compute
    sensitivity, specificity, F-score, receiver operating characteristics
    (ROC) curve, and percentile bootstrap confidence to conclude that the
    proposed scheme generalizes well and is a suitable approach for
    automatic seizure detection at a moderate cost, also opening the
    possibility of formulating new criteria to detect, classify or analyze
    abnormal EEGs.