Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia.
Infantile spasms (ISS) is a devastating epileptic syndrome that affects children under the age of 1 year. The diagnosis of ISS is based on the semiology of the seizure and the electroencephalogram (EEG) background characterized by hypsarrhythmia (HYPS). However, even skilled electrophysiologists may...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 9; pp. 1659 - 1669 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2017
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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=124786152&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124786152 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2017 vid: 55 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124786152 124786152 143995046 NLM28185049 10.1007/s11517-017-1616-z NLM28185049 124786152 ppf: 1659 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia. aug: au: Traitruengsakul, Supachan Seltzer, Laurie Paciorkowski, Alex Ghoraani, Behnaz Seltzer, Laurie E Paciorkowski, Alex R affil: Biomedical Engineering Department , Rochester Institute of Technology , Rochester USA sug: subj: Spasms, Infantile Physiopathology Epilepsy Physiopathology Algorithms Infant Seizures Physiopathology Software Electroencephalography Methods Infant: 1-23 months ab: Infantile spasms (ISS) is a devastating epileptic syndrome that affects children under the age of 1 year. The diagnosis of ISS is based on the semiology of the seizure and the electroencephalogram (EEG) background characterized by hypsarrhythmia (HYPS). However, even skilled electrophysiologists may interpret the EEG of children with ISS differently, and commercial software or existing epilepsy detection algorithms are not helpful. Since EEG is a key factor in the diagnosis of ISS, misinterpretation could result in serious consequences including inappropriate treatment. In this paper, we developed a novel algorithm to localize the relevant electrical abnormality known as epileptic discharges (or spikes) to provide a quantitative assessment of ISS in HYPS. The proposed algorithm extracts novel time-frequency features from the EEG signals and localizes the epileptic discharges associated with ISS in HYPS using a support vector machine classifier. We evaluated the proposed method on an EEG dataset with ISS subjects and obtained an average true positive and false negative of 98 and 7%, respectively, which was a significant improvement compared to the results obtained using the clinically available software. The proposed automated method provides a quantitative assessment of ISS in HYPS, which could significantly enhance our knowledge in therapy management of ISS. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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