Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection

dc.contributor.author	Πολυχρονάκη, Γεωργία Ε.	el
dc.contributor.author	Κτώνας, Περικλής Υ.	el
dc.contributor.author	Γκατζώνης, Στυλιανός	el
dc.contributor.author	Σιατούνη, Άννα Δ.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.date.accessioned	2015-02-09T10:43:54Z
dc.date.available	2015-02-09T10:43:54Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5913
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://iopscience.iop.org/	en
dc.subject	Electroencephalography
dc.subject	Synthetic signals
dc.subject	Ηλεκτροεγκεφαλογράφημα
dc.subject	Συνθετικά σήματα
dc.title	Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://lod.nal.usda.gov/19230
heal.contributorName	Τσέκου, Χαρά	el
heal.contributorName	Σακκάς, Διαμιανός	el
heal.contributorName	Νικήτα, Κωνσταντίνα Σ.	el
heal.identifier.secondary	doi:10.1088/1741-2560/7/4/046007
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2010
heal.bibliographicCitation	Polychronaki, G., Ktonas, P., Gatzonis, S., Siatouni, A., Asvestas, P., et al. (August 2010). Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection. Journal of Neural Engineering. 7(4). pp. 1-18. IOP Publishing Ltd: 2010. Available from: http://iopscience.iop.org/	en
heal.abstract	Fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of three waveform FD estimation algorithms (i.e. Katz's, Higuchi's and the k-nearest neighbour (k-NN) algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram (EEG). The selection of parameters involved in FD estimation, evaluation of the accuracy of the different algorithms and assessment of their robustness in the presence of noise were performed based on synthetic signals of known FD. When applied to scalp EEG data, Katz's and Higuchi's algorithms were found to be incapable of producing consistent changes of a single type (either a drop or an increase) during seizures. On the other hand, the k-NN algorithm produced a drop, starting close to the seizure onset, in most seizures of all patients. The k-NN algorithm outperformed both Katz's and Higuchi's algorithms in terms of robustness in the presence of noise and seizure onset detection ability. The seizure detection methodology, based on the k-NN algorithm, yielded in the training data set a sensitivity of 100% with 10.10 s mean detection delay and a false positive rate of 0.27 h−1, while the corresponding values in the testing data set were 100%, 8.82 s and 0.42 h−1, respectively. The above detection results compare favourably to those of other seizure onset detection methodologies applied to scalp EEG in the literature. The methodology described, based on the k-NN algorithm, appears to be promising for the detection of the onset of epileptic seizures based on scalp EEG.	en
heal.publisher	IOP Publishing Ltd	en
heal.journalName	Journal of Neural Engineering	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true