A neural NETWORK system for detection of atrial fibrillation in ambulatory electrocardiograms.
Introduction: A neural network classifier has been designed, which is able to distinguish atrial fibrillation (AF) from other supraventricular arrhythmias in ambulatory (Holter) ECGs. Method and Results: The classification algorithm uses a rhythm analysis that considers the ECG to be a time series o...
| Publicado en: | Journal of Cardiovascular Electrophysiology Vol. 5; no. 7; pp. 602 - 609 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Jul1994
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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=106088632&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106088632 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10453873 GSB jtl: Journal of Cardiovascular Electrophysiology issn: 10453873 maglogo: Y pubinfo: dt: Jul1994 vid: 5 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 106088632 2009418246 10.1111/j.1540-8167.1994.tb01301.x NLM7987530 106088632 ppf: 602 ppct: 7 formats: fmt: @attributes: type: P tig: atl: A neural NETWORK system for detection of atrial fibrillation in ambulatory electrocardiograms. aug: au: Cubanski D Cyganski D Antman EM Feldman CL sug: subj: Atrial Fibrillation Diagnosis Neural Networks (Computer) Electrocardiography, Ambulatory Predictive Value of Tests Human ab: Introduction: A neural network classifier has been designed, which is able to distinguish atrial fibrillation (AF) from other supraventricular arrhythmias in ambulatory (Holter) ECGs. Method and Results: The classification algorithm uses a rhythm analysis that considers the ECG to be a time series of RR interval durations. This is combined with an analysis of baseline morphology that considers the morphological characteristics of the non-QRS portions of the waveform. A backpropagation-based neural network has been used as part of the classifier implementation. When applied to a library consisting exclusively of 42,970 examples of AF and other supraventricular rhythm disturbances validated by an experienced cardiologist, the algorithm demonstrated a sensitivity of 82.4% for 10-beat runs of paroxysmal atrial fibrillation (PAF) and a specificity of 96.6%. Since this system has been implemented as a postprocessor to a conventional automated Holter system, operating only on segments of ECG that are known to contain supraventricular arrhythmias rather than ventricular arrhythmias or sinus rhythm, it can be added to most existing Holter processing systems without significantly increasing the average time to process a tape. Conclusion: A neural network system has been designed, which can potentially provide, for the first time, an accurate, quantitative technique to determine the natural history of PAF and to evaluate potential treatments for PAF. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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