Real-time epileptic seizure prediction based on online monitoring of pre-ictal features.
Reliable prediction of epileptic seizures is of prime importance as it can drastically change the quality of life for patients. This study aims to propose a real-time low computational approach for the prediction of epileptic seizures and to present an efficient hardware implementation of this appro...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 11; pp. 2461 - 2470 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2019
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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=139479827&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139479827 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2019 vid: 57 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139479827 139479827 NLM31478133 10.1007/s11517-019-02039-1 NLM31478133 139479827 ppf: 2461 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Real-time epileptic seizure prediction based on online monitoring of pre-ictal features. aug: au: Sadeghzadeh, Hoda Hosseini-Nejad, Hossein Salehi, Sina affil: Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran sug: subj: Seizures Diagnosis Electroencephalography Methods Diagnosis, Computer Assisted Methods Algorithms Sensitivity and Specificity Child, Preschool Infant Male Female Child Adolescence Young Adult Signal Processing, Computer Assisted Arthritis Impact Measurement Scales Ferrans and Powers Quality of Life Index Child, Preschool: 2-5 years Infant: 1-23 months Child: 6-12 years Adolescent: 13-18 years Male Female ab: Reliable prediction of epileptic seizures is of prime importance as it can drastically change the quality of life for patients. This study aims to propose a real-time low computational approach for the prediction of epileptic seizures and to present an efficient hardware implementation of this approach for portable prediction systems. Three levels of feature extraction are performed to characterize the pre-ictal activities of the EEG signal. In the first-level, the line length algorithm is applied to the pre-ictal region. The features obtained in the first-level are mathematically integrated to extract the second-level features and then the line lengths of the second-level features are calculated to obtain our third-level feature. The third-level information is compared with predefined threshold levels to make a decision on whether the extracted characteristics are relevant to a seizure occurrence or not. The validity of this algorithm was tested by EEG recordings in the CHB-MIT database (97 seizures, 834.224 h) for 19 epileptic patients. The results showed that the average sensitivity was 90.62%, the specificity was 88.34%, the accuracy was 88.76% with the average false prediction rate as low as 0.0046 h-1, and the average prediction time was 23.3 min. The low computational complexity is the superiority of the proposed approach, which provides a technologically simple but accurate way of predicting epileptic seizures and enables hardware implantable devices. Graphical abstract Proposed seizure prediction algorithm and its features. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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