Novel time-frequency-eigen filter for intraoperative neurophysiologic monitoring in spinal surgeries

Krishnatej Vedala, S. M.Amin Motahari, Mohammed Goryawala, Mercedes Cabrerizo, Ilker Yaylali, Malek Adjouadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages1578-1581
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Country/TerritoryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Novel time-frequency-eigen filter for intraoperative neurophysiologic monitoring in spinal surgeries'. Together they form a unique fingerprint.

Cite this