Literature DB >> 27454130

False alarms during patient monitoring in clinical intensive care units are highly related to poor quality of the monitored electrocardiogram signals.

Charalampos Tsimenidis1, Alan Murray.   

Abstract

Electrocardiograms (ECGs) recorded from patients in intensive care were investigated to quantify any relationship between ECG signal quality and false monitoring alarms. False alarms are a considerable problem for nursing and medical staff as they distract from clinical care, and are also a problem for patients as they disturb rest, which is important for clinical recovery. ECG and alarm data were obtained for 750 patient alarms from the PhysioNet database. The final 8 s period before the alarm was triggered was investigated. All but one ECG channel in 38 ECG recordings with out-of-range data were associated with false positive alarms (p  <  0.0001). The frequency contributions for baseline (BL) instability, electromyogram (EMG) muscle noise, and high frequency (HF) noise were calculated. For all three frequency bands, the contributions associated with false positive alarms were very significantly greater than for true positive alarms (p  <  0.0001). The greatest difference was for BL with a mean level for false positive alarms 4.0 times greater than for true positive alarms, followed by EMG and HF at 1.6 times and 1.4 times respectively. These results confirm that attention needs to be taken to improve ECG signal quality to reduce the frequency of clinical false alarms, and hence improve conditions for clinical staff and patients.

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Year:  2016        PMID: 27454130     DOI: 10.1088/0967-3334/37/8/1383

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  4 in total

1.  Evaluation of ECG algorithms designed to improve detect of transient myocardial ischemia to minimize false alarms in patients with suspected acute coronary syndrome.

Authors:  Michele M Pelter; Yuan Xu; Richard Fidler; Ran Xiao; David W Mortara; Hu Xiao
Journal:  J Electrocardiol       Date:  2017-10-24       Impact factor: 1.438

2.  Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurement.

Authors:  Diana P Tobon; Srinivasan Jayaraman; Tiago H Falk
Journal:  IEEE J Transl Eng Health Med       Date:  2017-12-04       Impact factor: 3.316

3.  False alarm reduction in critical care.

Authors:  Gari D Clifford; Ikaro Silva; Benjamin Moody; Qiao Li; Danesh Kella; Abdullah Chahin; Tristan Kooistra; Diane Perry; Roger G Mark
Journal:  Physiol Meas       Date:  2016-07-25       Impact factor: 2.833

4.  Monitoring significant ST changes through deep learning.

Authors:  Ran Xiao; Yuan Xu; Michele M Pelter; Richard Fidler; Fabio Badilini; David W Mortara; Xiao Hu
Journal:  J Electrocardiol       Date:  2018-08-01       Impact factor: 1.438

  4 in total

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