Fast Extraction of Somatosensory Evoked Potential Based on Robust Adaptive Filtering

Abstract

Somatosensory Evoked Potentials (SEPs) are brain electrical physiological signals elicited by the direct electrical stimulation of peripheral nerves. In other words, SEP is viewed as the nerve electric response produced by spinal cord sending or receiving sensory information in response to a stimulus (Turner et al., 2003). SEP has been widely used during the clinical testing and monitoring of the spinal cord and the central nervous system with the surface electrical stimulation. It can be said that the SEP is the most popular technique for intraoperative spinal cord monitoring in the operating room over 30 years (Nash et al., 1977; El-Hawary et al., 2006). However, in practice, the SEP signals recorded in the operating theaters are always contaminated by severe background noises (Krieger & Sclabassi 2001). The factors which cause noises may be electrical, physiological, anesthetic, surgical or abrupt event such as cough, body movement or adverse response to the stimulus of the patients. Generally, the recorded SEP signal is of a very poor signal-to-noise ratio (SNR) nature of the typical values between -20 dB to 0 dB (McGillem et al., 1981). Literature review of SEP extraction techniques showed that the Ensemble Averaging (EA) is the most commonly used practical technique for SEP extraction (MacLennan & Lovely 1995). Research studies reveal that the EA-SEP approach is a kind of stimulus-locked signal averaging method, which is able to enhance the SNR in evoked potential recordings when a huge number of independent stimulus trails are used (such as hundreds or more than one thousands stimuli). This means that the EA-SEP extraction may lengthen the surgical time and hinder the surgical procedures (El-Hawary et al., 2006). Furthermore, EA-SEP approach is lack of ability to provide the timely warning of the eminent danger of cord injury in spine surgeon monitoring. In conclusion, the major drawbacks of EA-SEP approach are: First, the assumption that the captured SEP signals are truly deterministic and invariant between ensembles is dubious. Actually, a number of studies showed that SEPs are nonstationary and time-varying across stimulus trails (Nishida et al., 1993; Woody, 1967). Second, the procedure is very time-consuming, requiring up to 2000 ensembles to identify the SEP signal, which causes the discomfort to the subjects, and brings larger opportunity for the interference to degrade the SEP extraction. Moreover, careful evaluation of the working principle of the EA-SEP method reveals that the averaging process may merge the details carrying the information of certain neurological function in SEP. With the analysis above, we can conclude that EA-SEP method may fail to track trial-to-trial variations both in