Literature DB >> 10224216

Concept for an intelligent anaesthesia EEG monitor.

W Nahm1, G Stockmanns, J Petersen, H Gehring, E Konecny, H D Kochs, E Kochs.   

Abstract

Considering the fundamental difficulties to define the term 'depth of anaesthesia', a more feasible concept for assessment of 'adequacy of anaesthesia' will be explained. The basic requirements for a monitoring index are definite response, gradual scaling and independence from the anaesthetic technique used. Additionally the index should be predictive for appearance of clinical signs of an inadequate anaesthesia. Different signal-processing methods will be discussed to extract the relevant information from both the spontaneous and the evoked brain electrical activity. In this context well established methods like spectral analysis are investigated in combination with new and more sophisticated methods like bispectral analysis or wavelet decomposition. Since no single-parameter index has been defined for monitoring depth of anaesthesia, a set of EEG parameters may be more useful to take into account intra- and interindividual variability. In parallel to the description of the monitor concept, the investigation of neural nets and fuzzy techniques, in addition to or in substitution of conventional statistical methods, will be introduced. Examples are given for data quality assessment, parameter extraction and re-classification.

Mesh:

Year:  1999        PMID: 10224216     DOI: 10.1080/146392399298492

Source DB:  PubMed          Journal:  Med Inform Internet Med        ISSN: 1463-9238


  4 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

2.  Estimation of medicine amount used anesthesia by an artificial neural network.

Authors:  Rüştü Güntürkün
Journal:  J Med Syst       Date:  2009-05-12       Impact factor: 4.460

3.  Determining the appropriate amount of anesthetic gas using DWT and EMD combined with neural network.

Authors:  Mustafa Coşkun; Hüseyin Gürüler; Ayhan Istanbullu; Musa Peker
Journal:  J Med Syst       Date:  2014-12-04       Impact factor: 4.460

4.  Monitoring anesthesia using neural networks: a survey.

Authors:  Claude Robert; Patrick Karasinski; Charles Daniel Arreto; Jean François Gaudy
Journal:  J Clin Monit Comput       Date:  2002 Apr-May       Impact factor: 2.502

  4 in total

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