Literature DB >> 28964425

A novel ECG detector performance metric and its relationship with missing and false heart rate limit alarms.

Chathuri Daluwatte1, Jose Vicente2, Loriano Galeotti3, Lars Johannesen4, David G Strauss4, Christopher G Scully5.   

Abstract

PURPOSE: Performance of ECG beat detectors is traditionally assessed on long intervals (e.g.: 30min), but only incorrect detections within a short interval (e.g.: 10s) may cause incorrect (i.e., missed+false) heart rate limit alarms (tachycardia and bradycardia). We propose a novel performance metric based on distribution of incorrect beat detection over a short interval and assess its relationship with incorrect heart rate limit alarm rates. BASIC PROCEDURES: Six ECG beat detectors were assessed using performance metrics over long interval (sensitivity and positive predictive value over 30min) and short interval (Area Under empirical cumulative distribution function (AUecdf) for short interval (i.e., 10s) sensitivity and positive predictive value) on two ECG databases. False heart rate limit and asystole alarm rates calculated using a third ECG database were then correlated (Spearman's rank correlation) with each calculated performance metric. MAIN
FINDINGS: False alarm rates correlated with sensitivity calculated on long interval (i.e., 30min) (ρ=-0.8 and p<0.05) and AUecdf for sensitivity (ρ=0.9 and p<0.05) in all assessed ECG databases. Sensitivity over 30min grouped the two detectors with lowest false alarm rates while AUecdf for sensitivity provided further information to identify the two beat detectors with highest false alarm rates as well, which was inseparable with sensitivity over 30min. PRINCIPAL
CONCLUSIONS: Short interval performance metrics can provide insights on the potential of a beat detector to generate incorrect heart rate limit alarms. Published by Elsevier Inc.

Entities:  

Keywords:  ECG beat detectors; False alarms; Heart rate limit alarms; Performance metrics

Mesh:

Year:  2017        PMID: 28964425      PMCID: PMC5965672          DOI: 10.1016/j.jelectrocard.2017.08.030

Source DB:  PubMed          Journal:  J Electrocardiol        ISSN: 0022-0736            Impact factor:   1.438


  11 in total

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Journal:  Circulation       Date:  2000-06-13       Impact factor: 29.690

2.  PhysioNet: a Web-based resource for the study of physiologic signals.

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Journal:  IEEE Eng Med Biol Mag       Date:  2001 May-Jun

3.  The impact of the MIT-BIH arrhythmia database.

Authors:  G B Moody; R G Mark
Journal:  IEEE Eng Med Biol Mag       Date:  2001 May-Jun

4.  Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms.

Authors:  Kelly Creighton Graham; Maria Cvach
Journal:  Am J Crit Care       Date:  2010-01       Impact factor: 2.228

5.  ECG beat detection using filter banks.

Authors:  V X Afonso; W J Tompkins; T Q Nguyen; S Luo
Journal:  IEEE Trans Biomed Eng       Date:  1999-02       Impact factor: 4.538

6.  Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database.

Authors:  P S Hamilton; W J Tompkins
Journal:  IEEE Trans Biomed Eng       Date:  1986-12       Impact factor: 4.538

7.  Novel approach to cardiac alarm management on telemetry units.

Authors:  Deborah A Whalen; Patricia M Covelle; James C Piepenbrink; Karen L Villanova; Charlotte L Cuneo; Eric H Awtry
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Review 8.  Systematic Review of Physiologic Monitor Alarm Characteristics and Pragmatic Interventions to Reduce Alarm Frequency.

Authors:  Christine Weirich Paine; Veena V Goel; Elizabeth Ely; Christopher D Stave; Shannon Stemler; Miriam Zander; Christopher P Bonafide
Journal:  J Hosp Med       Date:  2015-12-14       Impact factor: 2.960

9.  Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients.

Authors:  Barbara J Drew; Patricia Harris; Jessica K Zègre-Hemsey; Tina Mammone; Daniel Schindler; Rebeca Salas-Boni; Yong Bai; Adelita Tinoco; Quan Ding; Xiao Hu
Journal:  PLoS One       Date:  2014-10-22       Impact factor: 3.240

10.  An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave.

Authors:  Ikaro Silva; George B Moody
Journal:  J Open Res Softw       Date:  2014-09-24
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