Literature DB >> 25077767

An effective solution for capturing the single twitch of muscle: application to monitor muscle relaxation.

Omer H Colak1, Emmanuelle Girard, Eric Krejci.   

Abstract

In this study, a fast algorithm was developed to capture of train of four and to filter extra contraction and noises. A low pass filter created to filter extra contraction and high frequency noises. Then, a TCA algorithm designed to capturing of the single twitch of muscle. The algorithm updated to remove embedded extra contraction and to derive boundary values in this location from cubic spline interpolation. Efficiency of TCA and effect of extra contraction tested in time and frequency domain.

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Mesh:

Year:  2014        PMID: 25077767     DOI: 10.1007/s10916-014-0114-1

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  5 in total

1.  Control of sevoflurane anesthetic agent via neural network using electroencephalogram signals during anesthesia.

Authors:  Mustafa Tosun; Abdullah Ferikoğlu; Rüştü Güntürkün; Cevat Unal
Journal:  J Med Syst       Date:  2010-04-23       Impact factor: 4.460

Review 2.  Brief review: Neuromuscular monitoring: an update for the clinician.

Authors:  Thomas M Hemmerling; Nhien Le
Journal:  Can J Anaesth       Date:  2007-01       Impact factor: 5.063

3.  E-Nose system for anesthetic dose level detection using artificial neural network.

Authors:  Hamdi Melih Saraoğlu; Burçak Edin
Journal:  J Med Syst       Date:  2007-12       Impact factor: 4.460

Review 4.  Monitoring of neuromuscular function.

Authors:  H H Ali; J J Savarese
Journal:  Anesthesiology       Date:  1976-08       Impact factor: 7.892

5.  A new mouse model for the slow-channel congenital myasthenic syndrome induced by the AChR εL221F mutation.

Authors:  Frédéric Chevessier; Christoph Peter; Ulrike Mersdorf; Emmanuelle Girard; Eric Krejci; Joseph J McArdle; Veit Witzemann
Journal:  Neurobiol Dis       Date:  2011-12-08       Impact factor: 5.996

  5 in total

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