ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit.
We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containin...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 32; no. 1; pp. 53 - 62 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2018
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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=127064709&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127064709 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Feb2018 vid: 32 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127064709 127064709 144067542 NLM28210934 10.1007/s10877-017-9999-9 NLM28210934 127064709 ppf: 53 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit. aug: au: Rebergen, Dennis J. Nagaraj, Sunil B. Rosenthal, Eric S. Bianchi, Matt T. van Putten, Michel J. A. M. Westover, M. Brandon affil: Neurology Department, Massachusetts General Hospital, 55 Fruit Street, 02114, Boston, MA, USA sug: subj: Electrocardiography Heart Rate Physiology Intensive Care Units, Neonatal Signal Processing, Computer Assisted Intensive Care, Neonatal Predictive Value of Tests Automation Artifacts Reproducibility of Results Critical Illness Algorithms ROC Curve Infant, Newborn Sensitivity and Specificity Software Infant, Newborn: birth-1 month ab: We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containing artifacts leading to missed or false positive R-peak detections. Next, we calculated the absolute value of the difference between two adjacent RRIs (adRRI), and obtained the empirical probability distributions of adRRI values for valid R-peaks and artifacts. From these, we calculated an optimal threshold for separating adRRI values arising from artifact versus non-artefactual data. We compared the performance of our method with the methods of Berntson and Clifford on the same data. We identified 257,458 R-peak detections, of which 235,644 (91.5%) were true detections and 21,814 (8.5%) arose from artifacts. Our method showed superior performance for detecting artifacts with sensitivity 100%, specificity 99%, precision 99%, positive likelihood ratio of 100 and negative likelihood ratio <0.001 compared to Berntson's and Clifford's method with a sensitivity, specificity, precision and positive and negative likelihood ratio of 99%, 78%, 82%, 4.5, 0.013 for Berntson's method and 55%, 98%, 96%, 27.5, 0.460 for Clifford's method, respectively. A novel algorithm using a patient-independent threshold derived from the distribution of adRRI values in ICU ECG data identifies artifacts accurately, and outperforms two other methods in common use. Furthermore, the threshold was calculated based on real data from critically ill patients and the algorithm is easy to implement. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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