Application of Multivariate Spectral F Test for Somatosensory Evoked Response Detection

Abstract

Somatosensory Evoked Potential (SEP) is an important tool for monitoring vascular and spine surgeries, and other clinical applications. However, morphological SEP identification is subjective. Then, statistical techniques, such as Local Spectral F Test (SFT), have been used for response detection. The multivariate extension of SFT employs more than one derivation and has been recently considered advantageous to identify response to visual stimulation. This work aims at evaluating the performance of Multivariate SFT (MSFT) applied to EEG signals from 40 volunteers during stimulation at 5 Hz and different numbers of derivations (N), comparing the detection rates (DR). Frequencies of interest f_o1 = 15 Hz and f_o2 = 100 Hz were used, as well as L = 6 neighbor components at the frequencies from 70 to 95 Hz and a 5%-significance level. The number of derivations varied from N = 1 to 6. The detection rates obtained using f_o1 were higher than those with f_o2, which corresponds to false positives, since no response is expected to occur at such frequency. For f_o1, half of the volunteers exhibited a monotonic increase of DR as N was augmented. For other volunteers, an oscillatory pattern was noticed in DR as new derivations were added, suggesting that raising N do not necessarily lead to improvement in MSFT performance. For f_o2, raising N caused an increase in the false-positives rate above significance level of 5%. This could be explained in part by the correlation among the employed derivations. Finally, MSFT showed promising results at SEP identifying.