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). 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.

Peak detection of somatosensory evoked potentials using an integrated principal component analysis-walsh method.

Clinical application of somatosensory evoked potentials (SSEP) in intraoperative neurophysiological monitoring still requires anywhere between 200 to 500 trials, which is excessive and introduces a delay during surgery. In this study, the analysis was performed on the data recorded in 20 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. The first 10 trials were analyzed using an eigen decomposition technique, and a signal extraction algorithm eliminated the common components of the signals not contributing to the SSEP. A unique Walsh transform operation was then used to identify the position of the SSEP event within the clinical requirements of 10% time in latency deviation and 50% peak-to-peak amplitude deviation using only 10 trials. The algorithm also shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials.

An effective intra-operative neurophysiological monitoring scheme for aneurysm clipping and spinal fusion surgeries.

Somatosensory-evoked potentials (SSEPs) have been widely used for intra-operative neurophysiological monitoring (IONM). Currently at least 200-300 trials are required to generate a readable SSEP signal. This study introduces a novel approach that yields accurate detection results of the SSEP signal yet with a significantly reduced number of trials, resulting in an effectual monitoring process. The analysis was performed on data recorded in seven patients undergoing surgery, where the posterior tibial nerve was stimulated and the SSEP response was recorded from scalp electroencephalography using two bipolar electrodes, C(3)-C(4) and C(Z)-F(Z). The proposed approach employs an innovative, simple yet effective algorithm based on a patient-specific Gaussian template to detect the SSEP using only 30 trials. The time latencies of the P37 and N45 peaks are detected along with the peak-to-peak amplitudes. The time latencies are detected with a mean accuracy greater than 95%. Also, the P37 and N45 peak latencies and the peak-to-peak amplitude were found to be consistent throughout the surgical procedure within the 10% and 50% acceptable clinical limits, respectively. The results obtained support the assertion that the algorithm is capable of detecting SSEPs with high accuracy and consistency throughout the entire surgical procedure using only 30 trials.