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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tzallas, A. T. | en |
| dc.contributor.author | Karvelis, P. S. | en |
| dc.contributor.author | Katsis, C. D. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Giannopoulos, S. | en |
| dc.contributor.author | Konitsiotis, S. | en |
| dc.date.accessioned | 2015-11-24T17:33:14Z | - |
| dc.date.available | 2015-11-24T17:33:14Z | - |
| dc.identifier.issn | 0026-1270 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13836 | - |
| dc.rights | Default Licence | - |
| dc.subject | eeg | en |
| dc.subject | automated epilepsy diagnosis | en |
| dc.subject | clustering | en |
| dc.subject | artificial neural networks | en |
| dc.subject | spike detection | en |
| dc.subject | knowledge-based system | en |
| dc.subject | artificial neural-network | en |
| dc.subject | automatic spike detection | en |
| dc.subject | epileptiform discharges | en |
| dc.subject | raw eeg | en |
| dc.subject | recognition | en |
| dc.subject | system | en |
| dc.subject | quantification | en |
| dc.subject | algorithms | en |
| dc.subject | seizures | en |
| dc.title | A method for classification of transient events in EEG recordings: Application to epilepsy diagnosis | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.secondary | <Go to ISI>://000242859600005 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2006 | - |
| heal.abstract | Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories. epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is adventageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy. | en |
| heal.journalName | Methods Inf Med | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
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