ArticleHealth information science and systems2020
Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform.
Article in Health information science and systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting.Health information science and systems · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine if there is a difference in the wavelet variances of seizure and non-seizure channels in the EEG of an epileptic subject.
methodsA six-level decomposition was applied using the Maximal Overlap Discrete Wavelet Transform (MODWT). The wavelet variance and 95% CIs were calculated for each level of the decomposition. The number of changes in variance for each level were found using a change-point detection method of Whitcher. The Kruskal-Wallis test was used to determine if there were differences in the median number of change points within channels and across frequency bands (levels).
resultsNo distinctive pattern was found for the wavelet variances to differentiate the seizure and non-seizure channels. The seizure channels tended to have lower variances for each level and overall, but this pattern only held for one of the three seizure channels (RAST4). The median number of change points did not differ between the seizure and non-seizure channels either within each channel or across the frequency bands.
conclusionThe use of the MODWT in examining the variances and changes in variance did not show specific patterns which differentiate between seizure and non-seizure channels.
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