Dataset Information

Dataset of manually measured QT intervals in the electrocardiogram.

ABSTRACT:

Background

The QT interval and the QT dispersion are currently a subject of considerable interest. Cardiac repolarization delay is known to favor the development of arrhythmias. The QT dispersion, defined as the difference between the longest and the shortest QT intervals or as the standard deviation of the QT duration in the 12-lead ECG is assumed to be reliable predictor of cardiovascular mortality. The seventh annual PhysioNet/Computers in Cardiology Challenge, 2006 addresses a question of high clinical interest: Can the QT interval be measured by fully automated methods with accuracy acceptable for clinical evaluations?

Method

The PTB Diagnostic ECG Database was given to 4 cardiologists and 1 biomedical engineer for manual marking of QRS onsets and T-wave ends in 458 recordings. Each recording consisted of one selected beat in lead II, chosen visually to have minimum baseline shift, noise, and artifact.In cases where no T wave could be observed or its amplitude was very small, the referees were instructed to mark a 'group-T-wave end' taking into consideration leads with better manifested T wave.A modified Delphi approach was used, which included up to three rounds of measurements to obtain results closer to the median.

Results

A total amount of 2*5*548 Q-onsets and T-wave ends were manually marked during round 1. To obtain closer to the median results, 8.58 % of Q-onsets and 3.21 % of the T-wave ends had to be reviewed during round 2, and 1.50 % Q-onsets and 1.17 % T-wave ends in round 3. The mean and standard deviation of the differences between the values of the referees and the median after round 3 were 2.43 +/- 0.96 ms for the Q-onset, and 7.43 +/- 3.44 ms for the T-wave end.

Conclusion

A fully accessible, on the Internet, dataset of manually measured Q-onsets and T-wave ends was created and presented in additional file: 1 (Table 4) with this article. Thus, an available standard can be used for the development of automated methods for the detection of Q-onsets, T-wave ends and for QT interval measurements.

SUBMITTER: Christov I

PROVIDER: S-EPMC1524770 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

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Publications

Dataset of manually measured QT intervals in the electrocardiogram.

Christov Ivaylo I Dotsinsky Ivan I Simova Iana I Prokopova Rada R Trendafilova Elina E Naydenov Stefan S

Biomedical engineering online 20060518

<h4>Background</h4>The QT interval and the QT dispersion are currently a subject of considerable interest. Cardiac repolarization delay is known to favor the development of arrhythmias. The QT dispersion, defined as the difference between the longest and the shortest QT intervals or as the standard deviation of the QT duration in the 12-lead ECG is assumed to be reliable predictor of cardiovascular mortality. The seventh annual PhysioNet/Computers in Cardiology Challenge, 2006 addresses a questi ...[more]

PMID: 16707025

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Project description:AimsTo identify the potential sources of inaccuracy in manually measured adult respiratory rate (RR) data and quantify their effects.DesignQuantitative systematic review with meta-analyses where appropriate.Data sourcesMedline, CINAHL, and Cochrane Library (from database inception to 31 July 2019).Review methodsStudies presenting data on individual sources of inaccuracy in the manual measurement of adult RR were analysed, assessed for quality, and grouped according to the source of inaccuracy investigated. Quantitative data were extracted and synthesized and meta-analyses performed where appropriate.ResultsIncluded studies (N = 49) identified five sources of inaccuracy. The awareness effect creates an artefactual reduction in actual RR, and observation methods involving shorter counts cause systematic underscoring. Individual RR measurements can differ substantially in either direction between observations due to inter- or intra-observer variability. Value bias, where particular RRs are over-represented (suggesting estimation), is a widespread problem. Recording omission is also widespread, with higher average rates in inpatient versus triage/admission contexts.ConclusionThis review demonstrates that manually measured RR data are subject to several potential sources of inaccuracy.ImpactRR is an important indicator of clinical deterioration and commonly included in track-and-trigger systems. However, the usefulness of RR data depends on the accuracy of the observations and documentation, which are subject to five potential sources of inaccuracy identified in this review. A single measurement may be affected by several factors. Hence, clinicians should interpret recorded RR data cautiously unless systems are in place to ensure its accuracy. For nurses, this includes counting rather than estimating RRs, employing 60-s counts whenever possible, ensuring patients are unaware that their RR is being measured, and documenting the resulting value. For any given site, interventions to improve measurement should take into account the local organizational and cultural context, available resources, and the specific measurement issues that need to be addressed.

Dataset Information

Dataset of manually measured QT intervals in the electrocardiogram.

Background

Method

Results

Conclusion

Publications

Dataset of manually measured QT intervals in the electrocardiogram.

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