title: A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection.
abstract: 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.affiliation: Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India.
authors.forename: Priyanka
authors.initials: P
authors.lastname: Sharma
authors.affiliation: Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India.
authors.forename: Yusuf Uzzaman
authors.initials: YU
authors.lastname: Khan
authors.affiliation: Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India omarfarooq70@gmail.com.
authors.forename: Omar
authors.initials: O
authors.lastname: Farooq
authors.affiliation: Department of Neurology, AIIMS, New Delhi, India.
authors.forename: Manjari
authors.initials: M
authors.lastname: Tripathi
authors.affiliation: Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210.
authors.forename: Hojjat
authors.initials: H
authors.lastname: Adeli
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted
keywords: cubical thresholding; dispersion measures; nonconvulsive seizure detection; wavelet denoising