Quasi-Stationarity of EEG for Intraoperative Monitoring during Spinal Surgeries

• Published in: TheScientificWorld Vol. 2014; no. 2014; pp. 1 - 8
• Main Authors: Adjouadi, Malek, Yaylali, Ilker, Cabrerizo, Mercedes, Goryawala, Mohammed, Motahari, S. M. Amin, Vedala, Krishnatej
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2014; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Electroencephalography; Electroencephalography - methods; Evoked Potentials, Somatosensory; Human subjects; Humans; Intracranial Aneurysm - surgery; Methods; Monitoring, Intraoperative - methods; Neurosurgical Procedures; Noise; Principal Component Analysis; Principal components analysis; Recording equipment; Reproducibility of Results; Signal Processing, Computer-Assisted; Spine; Spine - surgery; Studies; Surgery
• ISSN: 2356-6140; 1537-744X; 1537-744X
• DOI: 10.1155/2014/468269

We present a study and application of quasi-stationarity of electroencephalogram for intraoperative neurophysiological monitoring (IONM) and an application of Chebyshev timewindowing for preconditioning SSEP trials to retain the morphological characteristics ofsomatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarityof 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 toextract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under theclinical 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 theSSEP signals provide a much improved and effective neurophysiological monitoring process.