Automatic seizure detection based on kernel robust probabilistic collaborative representation.
Visual inspection of electroencephalogram (EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic colla...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 1; pp. 205 - 220 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| 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=133800695&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133800695 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2019 vid: 57 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133800695 133800695 NLM30076538 133800695 10.1007/s11517-018-1881-5 NLM30076538 133800695 ppf: 205 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic seizure detection based on kernel robust probabilistic collaborative representation. aug: au: Yu, Zuyi Zhou, Weidong Zhang, Fan Xu, Fangzhou Yuan, Shasha Leng, Yan Li, Yang Yuan, Qi affil: Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, 250358, Jinan, China sug: subj: Seizures Diagnosis Algorithms Probability Databases Signal Processing, Computer Assisted Electroencephalography Automation Funding Source ab: Visual inspection of electroencephalogram (EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic collaborative representation-based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes (seizure and non-seizure), and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The wavelet transform with five scales is first employed to process the raw EEG signals. Next, the test EEG samples are collaboratively represented on the training sets by the kernel R-ProCRC and they are categorized by checking which class has the maximum likelihood. Finally, post-processing is deployed to reduce misjudgment and acquire more stable results. This method is evaluated on two EEG databases and yields an accuracy of 99.3% for interictal and ictal EEGs on the Bonn database. In addition, the average sensitivity of 97.48% and specificity of 96.81% are achieved from the Freiburg database. Graphical abstract Visual inspection of EEG recordings for epilepsy diagnosis is very time-consuming. Therefore, many researchers are devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this paper, a kernel version of the robust probabilistic collaborative representation based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals. The kernel R-ProCRC jointly maximizes the likelihood that a test EEG sample belongs to each of the two classes, i.e., seizure and non-seizure, and uses the kernel function method to map the EEG samples into the higher dimensional space to relieve the problem that they are linearly non-separable in the original space. The main procedures of the proposed method are exhibited in the two figures as following, Fig. 1 The main procedures of the proposed method. (a) The schematic diagram of EEG classification based on the Freiburg database. (b) The detailed procedures of the kernel R-ProCRC This method has been evaluated on two different types of EEG databases and shows superior performance. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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