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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Manis, G. | en |
| dc.contributor.author | Nikolopoulos, S. | en |
| dc.contributor.author | Alexandridi, A. | en |
| dc.contributor.author | Davos, C. | en |
| dc.date.accessioned | 2015-11-24T17:01:33Z | - |
| dc.date.available | 2015-11-24T17:01:33Z | - |
| dc.identifier.issn | 0010-4825 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10941 | - |
| dc.rights | Default Licence | - |
| dc.subject | heart rate variability | en |
| dc.subject | prediction | en |
| dc.subject | approximation | en |
| dc.subject | mean errors cardiogram classification | en |
| dc.subject | ecg | en |
| dc.subject | heart-rate-variability | en |
| dc.subject | nonlinear dynamics | en |
| dc.subject | failure | en |
| dc.subject | chaos | en |
| dc.subject | mortality | en |
| dc.subject | period | en |
| dc.title | Assessment of the classification capability of prediction and approximation methods for HRV analysis | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.compbiomed.2006.06.008 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2007 | - |
| heal.abstract | The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient data, as well as members of different age groups. The prediction methods are local linear prediction, local exponential prediction, the delay times method, autoregressive prediction and neural networks. Approximation is computed with local linear approximation, least squares approximation, neural networks and the wavelet transform. These methods are chosen since each has a different physical basis and thus extracts and uses time series information in a different way. (c) 2006 Elsevier Ltd. All rights reserved. | en |
| heal.journalName | Comput Biol Med | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Manis-2007-Assessment of the cl.pdf | 972.14 kB | Adobe PDF | View/Open Request a copy |
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