Literature DB >> 8953623

Assessing ECG signal quality on a coronary care unit.

J Allen1, A Murray.   

Abstract

Poor electrocardiograph (ECG) signal quality is associated with an increase in the number of false alarms, may degrade diagnostic information, and can increase the workload for coronary care unit (CCU) or other intensive care staff. It is important therefore to establish simple quantitative measures that can be used to demonstrate signal quality problems. In this study, a fixed-gain diagnostic bandwidth ECG from patients in a single CCU bed was monitored continuously for 10 weeks and measures which could relate to quality were calculated. These measures were the number of times the ECG exceeded a preset limit (out-of-range events, +/-4 mV) and the frequency content of the ECG plus superimposed noise in six different bandwidths (0.05-0.25, 0.25-10, 10-20, 20-48, 48-52, and 52-100 Hz). A computer-based data collection system calculated a 10 s average for each of the measures and logged these to memory. Following the data collection phase, good-quality baseline levels for the seven measures were calculated for each of the days studied and compared with levels during the evening-night (6 pm-6 am), which were in turn compared with levels during the day-time (6 am-6 pm). All measures were significantly lower (p < 0.001) for the selected good-quality ECGs compared with those recorded during the night, with low frequency, lower ECG bandwidth, and out-of-range events producing the greatest differences. Night-time noise levels were lower than day-time levels, and the largest reductions were found in the rate of out-of-range events (19.8 to 9.3 h-1) (p < 0.02), and low-frequency content less than 0.25 Hz (70 to 56 microV) (p < 0.01). Significant reductions during the night were also found in the lower ECG bandwidth 0.25-10 Hz (111 to 98 microV) (p < 0.01). No significant changes were found in the higher frequencies. We conclude that the low-frequency content and rate of out-of-range events are easy to obtain and could be used as measures for evaluating signal quality.

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Mesh:

Year:  1996        PMID: 8953623     DOI: 10.1088/0967-3334/17/4/002

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


  6 in total

Review 1.  A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters.

Authors:  Nicolò Gambarotta; Federico Aletti; Giuseppe Baselli; Manuela Ferrario
Journal:  Med Biol Eng Comput       Date:  2016-02-23       Impact factor: 2.602

2.  Signal quality estimation with multichannel adaptive filtering in intensive care settings.

Authors:  Ikaro Silva; Joon Lee; Roger G Mark
Journal:  IEEE Trans Biomed Eng       Date:  2012-06-14       Impact factor: 4.538

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.  Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter.

Authors:  Q Li; R G Mark; G D Clifford
Journal:  Physiol Meas       Date:  2007-12-10       Impact factor: 2.833

5.  Machine Learning and Decision Support in Critical Care.

Authors:  Alistair E W Johnson; Mohammad M Ghassemi; Shamim Nemati; Katherine E Niehaus; David A Clifton; Gari D Clifford
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2016-01-25       Impact factor: 10.961

6.  SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation.

Authors:  Zhidong Zhao; Yefei Zhang
Journal:  Front Physiol       Date:  2018-06-14       Impact factor: 4.566

  6 in total

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