Mouse EEG spike detection based on the adapted continuous wavelet transform

Tieng, Quang M., Kharatishvili, Irina, Chen, Min and Reutens, David C. (2016) Mouse EEG spike detection based on the adapted continuous wavelet transform. Journal of Neural Engineering, 13 2: . doi:10.1088/1741-2560/13/2/026018

Author Tieng, Quang M.
Kharatishvili, Irina
Chen, Min
Reutens, David C.
Title Mouse EEG spike detection based on the adapted continuous wavelet transform
Journal name Journal of Neural Engineering   Check publisher's open access policy
ISSN 1741-2552
Publication date 2016-02-09
Year available 2016
Sub-type Article (original research)
DOI 10.1088/1741-2560/13/2/026018
Open Access Status Not Open Access
Volume 13
Issue 2
Total pages 13
Place of publication Bristol, United Kingdom
Publisher Institute of Physics Publishing
Collection year 2017
Language eng
Formatted 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.
Keyword Electroencephalography
Spike detection
Wavelet transform
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Centre for Advanced Imaging Publications
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