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dc.contributor.authorMigliorelli Falcone, Carolina Mercedes
dc.contributor.authorAlonso López, Joan Francesc
dc.contributor.authorRomero Lafuente, Sergio
dc.contributor.authorNowak, Rafal
dc.contributor.authorRussi Tintoré, Antonio
dc.contributor.authorMañanas Villanueva, Miguel Ángel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2018-04-04T11:23:02Z
dc.date.available2018-04-04T11:23:02Z
dc.date.issued2017-08-01
dc.identifier.citationMigliorelli, C., Alonso, J.F., Romero, S., Nowak, R., Russi, A., Mañanas, M.A. Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors. "Journal of neural engineering", 1 Agost 2017, vol. 14, núm. 4, p. 2-15.
dc.identifier.issn1741-2560
dc.identifier.urihttp://hdl.handle.net/2117/115929
dc.description.abstractObjective. In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. Approach. Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. Main results. ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. Significance. The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of noninvasively recorded HFOs to help during pre-surgical planning and to reduce the need for invasive diagnostics.
dc.format.extent14 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshEpilepsy
dc.subject.lcshMagnetoencephalography
dc.subject.lcshBioengineering
dc.subject.lcshMedical technology
dc.subject.otherhigh frequency oscillations
dc.subject.otherepilepsy
dc.subject.otherMEG
dc.subject.otherripples
dc.subject.otherbeamformer
dc.titleAutomated detection of epileptic ripples in MEG using beamformer-based virtual sensors
dc.typeArticle
dc.subject.lemacTractament del senyal
dc.subject.lemacBioenginyeria
dc.subject.lemacTecnologia mèdica
dc.subject.lemacEpilèpsia
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.identifier.doi10.1088/1741-2552/aa684c
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://iopscience.iop.org/article/10.1088/1741-2552/aa684c/meta;jsessionid=E50E983429833A84967664D178F3AED8.c4.iopscience.cld.iop.org
dc.rights.accessOpen Access
local.identifier.drac21477866
dc.description.versionPostprint (author's final draft)
local.citation.authorMigliorelli, C.; Alonso, J.F.; Romero, S.; Nowak, R.; Russi, A.; Mañanas, M.A.
local.citation.publicationNameJournal of neural engineering
local.citation.volume14
local.citation.number4
local.citation.startingPage2
local.citation.endingPage15


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