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Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

Author

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  • Grubov, V.V.
  • Sitnikova, E.
  • Pavlov, A.N.
  • Koronovskii, A.A.
  • Hramov, A.E.

Abstract

Epileptic activity in the form of spike–wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time–frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal’s waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat’s EEG.

Suggested Citation

  • Grubov, V.V. & Sitnikova, E. & Pavlov, A.N. & Koronovskii, A.A. & Hramov, A.E., 2017. "Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 206-217.
    • Handle: RePEc:eee:phsmap:v:486:y:2017:i:c:p:206-217
    DOI: 10.1016/j.physa.2017.05.091
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    Cited by:

    1. Sun, Biao & Lv, Jia-Jun & Rui, Lin-Ge & Yang, Yu-Xuan & Chen, Yun-Gang & Ma, Chao & Gao, Zhong-Ke, 2021. "Seizure prediction in scalp EEG based channel attention dual-input convolutional neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

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