A novel signal processing method for intraoperative neurophysiological monitoring in spinal surgeries

Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded. An algorithm was developed using Chebychev 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 quasi-stationarity 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 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 undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm takes 4sec 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.

Development of micro immunosensors to study genomic and proteomic biomarkers related to cancer and Alzheimer's disease

A report from the National Institutes of Health defines a disease biomarker as a “characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” Early diagnosis is a crucial factor for incurable disease such as cancer and Alzheimer’s disease (AD). During the last decade researchers have discovered that biochemical changes caused by a disease can be detected considerably earlier as compared to physical manifestations/symptoms. In this dissertation electrochemical detection was utilized as the detection strategy as it offers high sensitivity/specificity, ease of operation, and capability of miniaturization and multiplexed detection. Electrochemical detection of biological analytes is an established field, and has matured at a rapid pace during the last 50 years and adapted itself to advances in micro/nanofabrication procedures. Carbon fiber microelectrodes were utilized as the platform sensor due to their high signal to noise ratio, ease and low-cost of fabrication, biocompatibility, and active carbon surface which allows conjugation with biorecognition moieties. This dissertation specifically focuses on the detection of 3 extensively validated biomarkers for cancer and AD. Firstly, vascular endothelial growth factor (VEGF) a cancer biomarker was detected using a one-step, reagentless immunosensing strategy. The immunosensing strategy allowed a rapid and sensitive means of VEGF detection with a detection limit of about 38 pg/mL with a linear dynamic range of 0–100 pg/mL. Direct detection of AD-related biomarker amyloid beta (Aβ) was achieved by exploiting its inherent electroactivity. The quantification of the ratio of Aβ1-40/42 (or Aβ ratio) has been established as a reliable test to diagnose AD through human clinical trials. Triple barrel carbon fiber microelectrodes were used to simultaneously detect Aβ1-40 and Aβ1-42 in cerebrospinal fluid from rats within a detection range of 100nM to 1.2μM and 400nM to 1μM respectively. In addition, the release of DNA damage/repair biomarker 8-hydroxydeoxyguanine (8-OHdG) under the influence of reactive oxidative stress from single lung endothelial cell was monitored using an activated carbon fiber microelectrode. The sensor was used to test the influence of nicotine, which is one of the most biologically active chemicals present in cigarette smoke and smokeless tobacco.