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https://olympias.lib.uoi.gr/jspui/handle/123456789/13707Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tsipouras, M. G. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:32:18Z | - |
| dc.date.available | 2015-11-24T17:32:18Z | - |
| dc.identifier.issn | 0169-2607 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13707 | - |
| dc.rights | Default Licence | - |
| dc.subject | arrhythmia detection | en |
| dc.subject | heart rate variability | en |
| dc.subject | time-frequency analysis | en |
| dc.subject | threatening cardiac-arrhythmias | en |
| dc.subject | power spectrum analysis | en |
| dc.subject | ventricular-fibrillation | en |
| dc.subject | neural-networks | en |
| dc.subject | blood-pressure | en |
| dc.subject | wavelet transformation | en |
| dc.subject | cardiovascular control | en |
| dc.subject | signals | en |
| dc.subject | tachycardia | en |
| dc.subject | tachyarrhythmia | en |
| dc.title | Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/s0169-2607(03)00079-8 | - |
| heal.identifier.secondary | <Go to ISI>://000220446800001 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0169260703000798/1-s2.0-S0169260703000798-main.pdf?_tid=a4f551d106c7a80b395b409701ee83e3&acdnat=1339758735_a3437d7b1cb21730b59f6b380740ee4e | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2004 | - |
| heal.abstract | We have developed an automatic arrhythmia detection system, which is based on heart rate features only. Initially, the RR interval duration signal is extracted from ECG recordings and segmented into small intervals. The analysis is based on both time and time-frequency (t-f) features. Time domain measurements are extracted and several combinations between the obtained features are used for the training of a set of neural networks. Short time Fourier transform and several time-frequency distributions (TFD) are used in the t-f analysis. The features obtained are used for the training of a set of neural networks, one for each distribution. The proposed approach is tested using the MIT-BIH arrhythmia database and satisfactory results are obtained for both sensitivity and specificity (87.5 and 89.5%, respectively, for time domain analysis and 90 and 93%, respectively, for t-f domain analysis). (C) 2003 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 | |
|---|---|---|---|---|
| Tsipouras-2004-Automatic arrhythmia.pdf | 824.56 kB | Adobe PDF | View/Open Request a copy |
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