An effective novel patient specific Gaussian template based scheme for somatosensory evoked potential detection

Goryawala, Mohammed; Yaylali, Ilker; Cabrerizo, Mercedes; Vedala, Krishnatej; Adjouadi, Malek

doi:10.1109/spmb.2011.6120110

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…1 recordings so that any significant change can be detected and rectified quickly. We proposed two algorithms, one using a Gaussian template [5] and an algorithm based on principal component analysis (PCA) and Walsh transformation for fast SSEP construction and automated peak detection [6]. The present manuscript discusses the automation of the latter algorithm and the results of application of the algorithm for twelve different surgical procedures.…”

Section: Introductionmentioning

confidence: 99%

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

Vedala¹,

Motahari²,

Goryawala³

et al. 2013

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

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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.

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Section: Introductionmentioning

confidence: 99%