Quasi-Stationarity of EEG for Intraoperative Monitoring during Spinal Surgeries. (16th February 2014)

• Record Type: Journal Article
• Authors: Vedala, Krishnatej; Motahari, S. M. Amin; Goryawala, Mohammed; Cabrerizo, Mercedes; Yaylali, Ilker; Adjouadi, Malek
• Other Names: Gillman L. M. Academic Editor.; Karakitsos D. Academic Editor.; Papalois A. E. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-02-16
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/468269
• Languages: English
• ISSNs: 2356-6140

Abstract

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.