Literature DB >> 14643396

Wavelet analysis for neonatal electroencephalographic seizures.

Masaomi Kitayama1, Hiroshi Otsubo, Shahid Parvez, Abhay Lodha, Ethel Ying, Boriana Parvez, Ryouhei Ishii, Yuko Mizuno-Matsumoto, Reza A Zoroofi, O Carter Snead.   

Abstract

Electroencepholographs (EEGs) of neonatal seizures differ from those of children and adults. This study evaluated whether wavelet transform analysis, a nonstationary frequency analysis of EEG, can recognize and characterize neonatal seizures. Twenty-second segments were analyzed from 69 EEG seizures in 15 neonatal patients whose seizures lasted 10 seconds or longer. The wavelet transform results were examined, as were EEG seizure durations and dominant frequencies. The wavelet transform results were correlated with the occurrence, after an 18-month follow-up, of postneonatal seizures. Wavelet transform analysis identified 40 seizures (58%) with a "sustained dominant frequency component" that lasted 10 seconds or longer and 29 seizures without a sustained dominant frequency component. The mean seizure duration of the 40 seizures with sustained dominant frequency components was 63.3 seconds, longer than the mean duration (33.6 seconds) of the seizures without sustained dominant frequency components, P < 0.01. Eleven patients manifested postneonatal epileptic seizures. Fifty-two EEG seizures in these 11 patients revealed more sustained dominant frequency components (74%) than 17 seizures in the 4 patients without postneonatal seizures (only 12%), P < 0.05. Wavelet transform analysis can identify neonatal EEG seizures and characterize their epileptic components. The presence of sustained dominant frequency components may predict postneonatal epileptic seizures.

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Year:  2003        PMID: 14643396     DOI: 10.1016/s0887-8994(03)00277-7

Source DB:  PubMed          Journal:  Pediatr Neurol        ISSN: 0887-8994            Impact factor:   3.372


  10 in total

1.  Gaussian mixture models for classification of neonatal seizures using EEG.

Authors:  E M Thomas; A Temko; G Lightbody; W P Marnane; G B Boylan
Journal:  Physiol Meas       Date:  2010-06-28       Impact factor: 2.833

2.  Robust neonatal EEG seizure detection through adaptive background modeling.

Authors:  Andriy Temko; Geraldine Boylan; William Marnane; Gordon Lightbody
Journal:  Int J Neural Syst       Date:  2013-06-04       Impact factor: 5.866

3.  Inclusion of temporal priors for automated neonatal EEG classification.

Authors:  Andriy Temko; Nathan Stevenson; William Marnane; Geraldine Boylan; Gordon Lightbody
Journal:  J Neural Eng       Date:  2012-06-19       Impact factor: 5.379

4.  EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures.

Authors:  Andriy Temko; Climent Nadeu; William Marnane; Geraldine Boylan; Gordon Lightbody
Journal:  IEEE Trans Inf Technol Biomed       Date:  2011-06-16

5.  Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform.

Authors:  Jack L Follis; Dejian Lai
Journal:  Health Inf Sci Syst       Date:  2020-09-15

6.  EEG-based neonatal seizure detection with Support Vector Machines.

Authors:  A Temko; E Thomas; W Marnane; G Lightbody; G Boylan
Journal:  Clin Neurophysiol       Date:  2010-08-14       Impact factor: 3.708

7.  Clinical implementation of a neonatal seizure detection algorithm.

Authors:  Andriy Temko; William Marnane; Geraldine Boylan; Gordon Lightbody
Journal:  Decis Support Syst       Date:  2015-02       Impact factor: 5.795

8.  Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis.

Authors:  Zabit Hameed; Saqib Saleem; Jawad Mirza; Muhammad Salman Mustafa
Journal:  PLoS One       Date:  2018-01-19       Impact factor: 3.240

9.  Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram.

Authors:  Hamid Abbasi; Charles P Unsworth
Journal:  Neural Regen Res       Date:  2020-02       Impact factor: 5.135

10.  A method for AI assisted human interpretation of neonatal EEG.

Authors:  Sergi Gomez-Quintana; Alison O'Shea; Andreea Factor; Emanuel Popovici; Andriy Temko
Journal:  Sci Rep       Date:  2022-06-29       Impact factor: 4.996

  10 in total

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