Quasi-stationarity of EEG for intraoperative monitoring during spinal surgeries

Krishnatej Vedala, S. M.Amin Motahari, Mohammed Goryawala, Mercedes Cabrerizo, Ilker Yaylali, Malek Adjouadi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We present a study and application of quasi-stationarity of electroencephalogram for intraoperative neurophysiological monitoring (IONM) and an application of Chebyshev time windowing for preconditioning SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. This method is shown empirically to be more clinically viable than present day approaches. In all twelve cases, the algorithm takes 4 sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

Original languageEnglish (US)
Article number468269
JournalThe Scientific World Journal
Volume2014
DOIs
StatePublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)

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