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.

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.

No abstract

My mentor Dr. Malek Adjouadi has taught me a great deal in professional, personal and intellectual development. He has always had my best interest at heart and has provided me with every opportunity to grow. I am grateful for all the kindness and patience that he has shown me over the years. I would like to thank my gurus, Dr.Armando Barreto, Dr. Jean Andrian and Dr. Naphtalie Rishe, naming a few, whose trials to obtain an SSEP signal, which is excessive and introduces a significant delay to prevent potential neurological risks during surgery. The main objective of this dissertation is to develop a means to obtain the SSEP signal using a much reduced number of trials (20 trials or less) while still optimizing the effectiveness of the monitoring system. The preliminary research steps were to determine those characteristics that distinguish the SSEP with the ongoing brain activity. We first established that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded.A novel algorithm is subsequently developed using Chebyshev time windowing for preconditioning of 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 quasistationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised vii when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials.In this study, the analysis was performed on the data recorded in 29 patients who underwent surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp EEG. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm took on an average 4sec to extract an SSEP signal, as compared to conventional methods, which take up to 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 provided for a much improved and effective neurophysiological monitoring process.

We present a novel signal-processing algorithm to extract the posterior tibial somatosensory evoked potentials (tSSEP) using a minimum number of trials. We analyze the proposed algorithm and compare it with the clinically used conventional signal averaging method for 12 surgical procedures. The tSSEP trials are continuously fed to our processing algorithm that displays the extracted SSEP after processing 12 successive unrejected sweeps. A unique filtering process employing time, frequency and eigen systems, in that order, was used to extract the SSEP from this set of 12 trials. The algorithm then detects, marks and records the P37 and N45 peaks using the first order differentials obtained through Walsh transformation. The monitoring using the algorithm was successful and proved conclusive to the clinical information through the different surgical procedures. Higher accuracy and faster execution time in determining the SSEP signals provides for a much improved and effective neurophysiological monitoring process.