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Application of Multivariate Spectral F Test for Somatosensory Evoked Response Detection

  • Karina Miranda BosonEmail author
  • Antonio Mauricio Ferreira Leite Miranda de Sá
  • Danilo Barbosa Melges
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

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 fo1 = 15 Hz and fo2 = 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 fo1 were higher than those with fo2, which corresponds to false positives, since no response is expected to occur at such frequency. For fo1, 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 fo2, 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.

Keywords

Somatosensory evoked potential Multivariate local spectral F test Objective response detection 

Notes

Conflict of Interest

The authors declare no conflict of interest.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Karina Miranda Boson
    • 1
    • 2
    Email author
  • Antonio Mauricio Ferreira Leite Miranda de Sá
    • 3
  • Danilo Barbosa Melges
    • 1
    • 4
  1. 1.Electrical Engineering DepartmentFederal University of Minas GeraisBelo HorizonteBrazil
  2. 2.Physical Therapy Undergraduate CourseFaculty of Medical Sciences of Minas GeraisBelo HorizonteBrazil
  3. 3.Biomedical Engineering ProgramFederal University of Rio de JaneiroRio de JaneiroBrazil
  4. 4.Graduate Program in Electrical EngineeringFederal University of Minas GeraisBelo HorizonteBrazil

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