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Variability of human-annotations of 12-lead ECG features collected using a web system: Students vs. practitioners

Cairns, Andrew W., Bond, Raymond, Breen, Cathal, Finlay, Dewar, Guldenring, Daniel and Peace, Aaron (2017) Variability of human-annotations of 12-lead ECG features collected using a web system: Students vs. practitioners. In: International Society for Computerised Electrocardiology, St. Simons Island. Elsevier. 1 pp. [Conference contribution]

Full text not available from this repository.

URL: http://www.jecgonline.com/article/S0022-0736(17)30286-8/fulltext

DOI: http://dx.doi.org/10.1016/j.jelectrocard.2017.08.041

Abstract

Introduction: The electrocardiogram (ECG) is often interpreted incorrectly with up to 33% of interpretations containing a significant error. The difficulty in ECG interpretation is two-fold; 1) it demands an extensive knowledge of cardiac physiology, and 2) the ECG inflates cognitive workload due to the complex nature of its presentation. To make a diagnosis, the reader is required to measure ECG features in order to contrast these annotations with diagnostic criteria. Whilst signal processing algorithms can provide automated measurements, they are often imprecise. Based on these observations, a web-based system was developed to allow the interpreter to measure and input their own ECG annotations. These annotations are then processed by a rule-based algorithm, which presents a set of suggested diagnoses.However, imprecise and inconsistent human annotations would affect both the reader's diagnostic decision making and also the accuracy of the diagnoses suggested by the algorithm (junk in = junk out). Our study measures the variability of manual annotations collected using our web system. Clinical physiology students (n = 10) and medical practitioners (n = 11) participated in our study.Results: Annotations for the same ECG are as follows (all participants = A, students = S, medical practitioners = P):Heart Rate (A[mean = 89.39 bpm, SD = 9.95], S[mean = 88.7 bpm, SD = 4.27], P[mean = 91.4 bpm, SD = 14.68], p = 0.73), P-wave duration (A[mean = 0.08 s, SD = 0.02], S[mean = 0.09 s, SD = 0.03], P[mean = 0.08 s, SD = 0.01], p = 0.39), P-wave amplitude (A[mean = 0.18 mv, SD = 0.04], S[mean = 0.19 mv, SD = 0.05], P[mean = 0.18 mv, SD = 0.3], p = 0.38), P-R interval (A[mean = 0.16 s, SD = 0.04], S[mean = 0.18 s, SD = 0.05], P[mean = 0.16 s, SD = 0.03], p = 0.49), cardiac axis (A[mean = 58.11°, SD = 13.23], S[mean = 60°, SD = 0], P[mean = 51.5°, SD = 18.8], p = 0.46), Q-T interval (A[mean = 0.32 s, SD = 0.14], S[mean = 0.41 s, SD = 0.06], P[mean = 0.24 s, SD = 0.17], p < 0.01), R-R interval (A[mean = 0.63 s, SD = 0.21], S[mean = 0.72 s, SD = 0.13], P[mean = 0.53 s, SD = 0.27], p = 0.06) and QTc (A[mean = 0.4 s, SD = 0.15], S[mean = 0.48 s, SD = 0.09], P[mean = 0.33 s, SD = 0.2], p = 0.02).Discussion: Students annotated more features (5/8) with less variance. Students annotate interval measurements with 47% less variation than medical practitioners (Σ interval measurement; students SD = 0.36, practitioners SD = 0.68). Students also had less variation in measuring heart rate, P-wave amplitude and cardiac axis. Two of the annotated features (QT-interval and QTc) from both cohorts were statistically different (p ≤ 0.05).Conclusion: In order to make an accurate diagnosis precise ECG annotations are required. This study determined the variability of manual ECG annotations on a cohort containing both students and practitioners.

Item Type:Conference contribution (Poster)
Keywords:ECG, medical informatics, cardiology
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Faculty of Life and Health Sciences
Faculty of Life and Health Sciences > School of Health Sciences
Research Institutes and Groups:Institute of Nursing and Health Research > Centre for Health and Rehabilitation Technologies
Institute of Nursing and Health Research
Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:39111
Deposited By: Dr Raymond Bond
Deposited On:19 Dec 2017 14:06
Last Modified:11 Jan 2018 11:47

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