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- W2067684706 abstract "Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant." @default.
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- W2067684706 date "2013-07-01" @default.
- W2067684706 modified "2023-09-23" @default.
- W2067684706 title "A study of morphology-based wavelet features and multiple-wavelet strategy for EEG signal classification: Results and selected statistical analysis" @default.
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- W2067684706 doi "https://doi.org/10.1109/embc.2013.6610919" @default.
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