Quasi-Stationarity of EEG for Intraoperative Monitoring during Spinal Surgeries
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/468269 |
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| Summary: | 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. |
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| ISSN: | 2356-6140 1537-744X |