Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/13736Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Oikonomou, V. P. | en |
| dc.contributor.author | Tzallas, A. T. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:32:28Z | - |
| dc.date.available | 2015-11-24T17:32:28Z | - |
| dc.identifier.issn | 0169-2607 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13736 | - |
| dc.rights | Default Licence | - |
| dc.subject | eeg | en |
| dc.subject | epilepsy | en |
| dc.subject | spike enhancement | en |
| dc.subject | non-stationary signal | en |
| dc.subject | kalman filter | en |
| dc.subject | artificial neural-networks | en |
| dc.subject | epileptiform activity | en |
| dc.subject | automatic recognition | en |
| dc.subject | interictal spikes | en |
| dc.subject | raw eeg | en |
| dc.subject | system | en |
| dc.subject | discharges | en |
| dc.subject | algorithms | en |
| dc.subject | models | en |
| dc.title | A Kalman filter based methodology for EEG spike enhancement | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.cmpb.2006.10.003 | - |
| heal.identifier.secondary | <Go to ISI>://000244167100002 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0169260706002422/1-s2.0-S0169260706002422-main.pdf?_tid=614e6609e4af2932248f3378f4d2f698&acdnat=1339758291_68653f35983137452030915329a4eb32 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2007 | - |
| heal.abstract | In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives. (c) 2006 Elsevier Ireland Ltd. All rights reserved. | en |
| heal.publisher | Elsevier | en |
| heal.journalName | Comput Methods Programs Biomed | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Oikonomou-2007-A Kalman filter base.pdf | 667.49 kB | Adobe PDF | View/Open Request a copy |
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