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https://olympias.lib.uoi.gr/jspui/handle/123456789/11008Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kampouraki, A. | en |
| dc.contributor.author | Manis, G. | en |
| dc.contributor.author | Nikou, C. | en |
| dc.date.accessioned | 2015-11-24T17:02:03Z | - |
| dc.date.available | 2015-11-24T17:02:03Z | - |
| dc.identifier.issn | 1089-7771 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11008 | - |
| dc.rights | Default Licence | - |
| dc.subject | feature extraction | en |
| dc.subject | heartbeat time series | en |
| dc.subject | heart rate variability (hrv) | en |
| dc.subject | support vector machine (svm) | en |
| dc.subject | rate-variability | en |
| dc.subject | idiopathic scoliosis | en |
| dc.subject | rate signal | en |
| dc.subject | dynamics | en |
| dc.subject | risk | en |
| dc.subject | recognition | en |
| dc.subject | algorithm | en |
| dc.subject | networks | en |
| dc.subject | images | en |
| dc.subject | bci | en |
| dc.title | Heartbeat Time Series Classification With Support Vector Machines | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Titb.2008.2003323 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2009 | - |
| heal.abstract | In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease. | 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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