Study of atrial activities for abnormality detection by phase rectified signal averaging technique.
Non-invasive detection of Atrial Fibrillation (AF) and Atrial Flutter (AFL) from ECG at the time of their onset can prevent forthcoming dangers for patients. In most of the previous detection algorithms, one of the steps includes filtering of the signal to remove noise and artefacts present in the s...
| Publicado en: | Journal of Medical Engineering & Technology Vol. 39; no. 5; pp. 291 - 303 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2015
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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=109602527&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109602527 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03091902 B9Q jtl: Journal of Medical Engineering & Technology issn: 03091902 maglogo: Y pubinfo: dt: Jul2015 vid: 39 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 109602527 NLM26084877 2013104062 10.3109/03091902.2015.1052108 NLM26084877 109602527 ppf: 291 ppct: 12 formats: tig: atl: Study of atrial activities for abnormality detection by phase rectified signal averaging technique. aug: au: Maji, U Pal, S Mitra, M affil: Department of Applied Electronics and Instrumentation Engineering, Haldia Institute of Technology , Haldia , India and. sug: ab: Non-invasive detection of Atrial Fibrillation (AF) and Atrial Flutter (AFL) from ECG at the time of their onset can prevent forthcoming dangers for patients. In most of the previous detection algorithms, one of the steps includes filtering of the signal to remove noise and artefacts present in the signal. In this paper, a method of AF and AFL detection is proposed from ECG without the conventional filtering stage. Here Phase Rectified Signal Average (PRSA) technique is used with a novel optimized windowing method to achieve an averaged signal without quasi-periodicities. Both time domain and statistical features are extracted from a novel SQ concatenated section of the signal for non-linear Support Vector Machine (SVM) based classification. The performance of the proposed algorithm is tested with the MIT-BIH Arrhythmia database and good performance parameters are obtained, as indicated in the result section. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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