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• W2287806398 startingPage "026018" @default.
• W2287806398 abstract "Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording." @default.
• W2287806398 created "2016-06-24" @default.
• W2287806398 creator A5012718640 @default.
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• W2287806398 date "2016-02-09" @default.
• W2287806398 modified "2023-09-26" @default.
• W2287806398 title "Mouse EEG spike detection based on the adapted continuous wavelet transform" @default.
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• W2287806398 doi "https://doi.org/10.1088/1741-2560/13/2/026018" @default.
• W2287806398 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26859447" @default.
• W2287806398 hasPublicationYear "2016" @default.
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