Document Detail


A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection.
MedLine Citation:
PMID:  24934269     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The detection of nonconvulsive seizures (NCSz) is a challenge because of the lack of physical symptoms, which may delay the diagnosis of the disease. Many researchers have reported automatic detection of seizures. However, few investigators have concentrated on detection of NCSz. This article proposes a method for reliable detection of NCSz. The electroencephalography (EEG) signal is usually contaminated by various nonstationary noises. Signal denoising is an important preprocessing step in the analysis of such signals. In this study, a new wavelet-based denoising approach using cubical thresholding has been proposed to reduce noise from the EEG signal prior to analysis. Three statistical features were extracted from wavelet frequency bands, encompassing the frequency range of 0 to 8, 8 to 16, 16 to 32, and 0 to 32 Hz. Extracted features were used to train linear classifier to discriminate between normal and seizure EEGs. The performance of the method was tested on a database of nine patients with 24 seizures in 80 hours of EEG recording. All the seizures were successfully detected, and false positive rate was found to be 0.7 per hour.
Authors:
Priyanka Sharma; Yusuf Uzzaman Khan; Omar Farooq; Manjari Tripathi; Hojjat Adeli
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-6-16
Journal Detail:
Title:  Clinical EEG and neuroscience     Volume:  -     ISSN:  1550-0594     ISO Abbreviation:  Clin EEG Neurosci     Publication Date:  2014 Jun 
Date Detail:
Created Date:  2014-6-17     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101213033     Medline TA:  Clin EEG Neurosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© EEG and Clinical Neuroscience Society (ECNS) 2014.
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