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Quasi-stationarity of EEG for intraoperative monitoring during spinal surgeries.
Vedala, Krishnatej; Motahari, S M Amin; Goryawala, Mohammed; Cabrerizo, Mercedes; Yaylali, Ilker; Adjouadi, Malek.
Afiliación
  • Vedala K; Center for Advanced Technology and Education, FIU College of Engineering and Computing, Room Number EC2672, 10555 West Flagler Street, Miami, FL 33174, USA.
  • Motahari SM; Center for Advanced Technology and Education, FIU College of Engineering and Computing, Room Number EC2672, 10555 West Flagler Street, Miami, FL 33174, USA.
  • Goryawala M; Center for Advanced Technology and Education, FIU College of Engineering and Computing, Room Number EC2672, 10555 West Flagler Street, Miami, FL 33174, USA.
  • Cabrerizo M; Center for Advanced Technology and Education, FIU College of Engineering and Computing, Room Number EC2672, 10555 West Flagler Street, Miami, FL 33174, USA.
  • Yaylali I; Department of Clinical Neurophysiology, Oregon Health and Science University, Portland, OR 97239-3098, USA.
  • Adjouadi M; Center for Advanced Technology and Education, FIU College of Engineering and Computing, Room Number EC2672, 10555 West Flagler Street, Miami, FL 33174, USA.
ScientificWorldJournal ; 2014: 468269, 2014.
Article en En | MEDLINE | ID: mdl-24695792
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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Algoritmos / Monitoreo Intraoperatorio / Procedimientos Neuroquirúrgicos / Electroencefalografía / Potenciales Evocados Somatosensoriales Límite: Humans Idioma: En Revista: ScientificWorldJournal Asunto de la revista: MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Algoritmos / Monitoreo Intraoperatorio / Procedimientos Neuroquirúrgicos / Electroencefalografía / Potenciales Evocados Somatosensoriales Límite: Humans Idioma: En Revista: ScientificWorldJournal Asunto de la revista: MEDICINA Año: 2014 Tipo del documento: Article País de afiliación: Estados Unidos