Literature DB >> 16705256

Centre-specific multichannel electrogastrographic testing utilizing wavelet-based decomposition.

I V Tchervensky1, R J de Sobral Cintra, E Neshev, V S Dimitrov, D C Sadowski, M P Mintchev.   

Abstract

Although the principles of electrogastrography (EGG) have been known for years, the clinical utility of EGG has not been clearly demonstrated, and EGG recording and analysis techniques have not been fully standardized. The aim of this study was to develop a multichannel EGG technique for detecting abnormal gastric motility using an EGG database specifically designed for a particular testing centre, maximizing the sensitivity and the specificity of the test. Eight healthy volunteers formed a reference group to which 4 study groups (17 gastro-oesophageal reflux disease (GORD) patients, 7 functional dyspepsia patients, 8 post-fundoplication patients and 12 healthy volunteers) were compared. Eight-channel EGG was recorded in the postprandial and fasting states for 30 min each. The recorded signals were wavelet compressed and the resulting error (per cent root mean square difference (PRD)) after the compression was utilized to compare the study groups to the reference group. A threshold in the number of channels with significantly different PRD values was introduced. Sensitivity (SE), specificity (SP) and correct classification rate (CC) of the test in recognizing each clinical condition in the study groups for several channel thresholds and compressions were calculated, and were maximized. Increasing the compression and channel threshold levels improved the specificity, but decreased the sensitivity of the multichannel EGG test. An optimal combination region was identified based on a centre-specific adjustment of the channel threshold and the wavelet compression. The achieved maximum sensitivity, specificity and correct classification for this region in our test centre were as follows: GORD--SE 82.4%, SP 83.3%, CC 82.8%; functional dyspepsia--SE 100%, SP 75%, CC 84.2%; post-fundoplication--SE 75.0%, SP 83.3%, CC 80.0%. The utilization of a wavelet-based decomposition technique to process multichannel EGG signals can be a very effective method for enhancing the clinical utility of EGG, provided it is specifically developed for a given testing centre.

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Year:  2006        PMID: 16705256     DOI: 10.1088/0967-3334/27/7/002

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  1 in total

1.  Detection of obstructive respiratory abnormality using flow-volume spirometry and radial basis function neural networks.

Authors:  Mahesh Veezhinathan; Swaminathan Ramakrishnan
Journal:  J Med Syst       Date:  2007-12       Impact factor: 4.460

  1 in total

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