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https://olympias.lib.uoi.gr/jspui/handle/123456789/13929Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tzallas, A. T. | en |
| dc.contributor.author | Tsipouras, M. G. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:34:06Z | - |
| dc.date.available | 2015-11-24T17:34:06Z | - |
| dc.identifier.issn | 1089-7771 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13929 | - |
| dc.rights | Default Licence | - |
| dc.subject | artificial neural networks (anns) | en |
| dc.subject | eeg | en |
| dc.subject | epilepsy | en |
| dc.subject | seizure detection | en |
| dc.subject | time-frequency (t-f) analysis | en |
| dc.subject | artificial neural-network | en |
| dc.subject | support vector machines | en |
| dc.subject | spike detection | en |
| dc.subject | signals classification | en |
| dc.subject | automatic detection | en |
| dc.subject | wavelet analysis | en |
| dc.subject | algorithms | en |
| dc.subject | diagnosis | en |
| dc.subject | features | en |
| dc.subject | long | en |
| dc.title | Epileptic Seizure Detection in EEGs Using Time-Frequency Analysis | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Titb.2009.2017939 | - |
| heal.identifier.secondary | <Go to ISI>://000269518900005 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2009 | - |
| heal.abstract | The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency (t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t-f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented. | en |
| heal.journalName | Ieee Transactions on Information Technology in Biomedicine | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
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