Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis.
In epilepsy diagnosis or epileptic seizure detection, much effort has been focused on finding effective combination of feature extraction and classification methods. In this paper, we develop a wavelet-based sparse functional linear model for representation of EEG signals. The aim of this modeling a...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 1/2; pp. 49 - 61 |
|---|---|
| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2013
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104068492&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104068492 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2013 vid: 51 iid: 1/2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104068492 NLM23054383 2012019384 10.1007/s11517-012-0967-8 NLM23054383 104068492 ppf: 49 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis. aug: au: Xie, Shengkun Krishnan, Sridhar affil: Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada, shengkun.xie@ryerson.ca. sug: subj: Electroencephalography Methods Seizures Diagnosis Seizures Physiopathology Signal Processing, Computer Assisted Databases Electroencephalography Classification Human Linear Regression Reproducibility of Results ab: In epilepsy diagnosis or epileptic seizure detection, much effort has been focused on finding effective combination of feature extraction and classification methods. In this paper, we develop a wavelet-based sparse functional linear model for representation of EEG signals. The aim of this modeling approach is to capture discriminative random components of EEG signals using wavelet variances. To achieve this goal, a forward search algorithm is proposed for determination of an appropriate wavelet decomposition level. Two EEG databases from University of Bonn and University of Freiburg are used for illustration of applicability of the proposed method to both epilepsy diagnosis and epileptic seizure detection problems. For this data considered, we show that wavelet-based sparse functional linear model with a simple classifier such as 1-NN classification method leads to higher classification results than those obtained using other complicated methods such as support vector machine. This approach produces a 100% classification accuracy for various classification tasks using the EEG database from University of Bonn, and outperforms many other state-of-the-art techniques. The proposed classification scheme leads to 99% overall classification accuracy for the EEG data from University of Freiburg. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|