Unsupervised classification of atrial heartbeats using a prematurity index and wave morphology features.
ECG heartbeat type detection and classification are regarded as important procedures since they can significantly help to provide an accurate automated diagnosis. This paper addresses the specific problem of detecting atrial premature beats, that had been demonstrated to be a marker for stroke risk...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 7; pp. 731 - 742 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104906627&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104906627 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2009 vid: 47 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104906627 NLM19184158 2010304517 10.1007/s11517-009-0435-2 NLM19184158 104906627 ppf: 731 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Unsupervised classification of atrial heartbeats using a prematurity index and wave morphology features. aug: au: Rodríguez-Sotelo JL Cuesta-Frau D Castellanos-Dominguez G Rodríguez-Sotelo, José Luis Cuesta-Frau, D Castellanos-Dominguez, G affil: G. Control y Procesamiento Digital de Señales, Universidad Nacional de Colombia, Campus La Nubia, Manizales, Colombia sug: subj: Arrhythmia Diagnosis Diagnosis, Computer Assisted Heart Rate Algorithms Arrhythmia Classification Human Reliability Sensitivity and Specificity ab: ECG heartbeat type detection and classification are regarded as important procedures since they can significantly help to provide an accurate automated diagnosis. This paper addresses the specific problem of detecting atrial premature beats, that had been demonstrated to be a marker for stroke risk or cardiac arrhythmias. The proposed methodology consists of a stage to estimate characteristics such as morphology of P wave and QRS complex as well as indices of prematurity and a non-supervised stage used by the algorithm J-means to separate heartbeat feature vectors into classes. Partition initialization is carried out by a Max-Min approach. Experimental data set is taken from MIT-BIH arrhythmia database. Results evidence the reliability of the method since achieved sensitivity and specificity are high, 92.9 and 99.6%, respectively, for an average output number of 12 discovered clusters that can be considered as appropriate value to separate heartbeat classes from recordings. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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