Key result
The proposed algorithm for calculating the spatial QRS-T angle reduced mean absolute errors to <4% and improved sensitivity and precision to >94% compared to the standard approach.
Why the study?
The spatial QRS-T angle is a strong risk predictor for ventricular arrhythmia and sudden cardiac death, but accurate estimation requires robust methods that overcome ECG delineation errors.
Does the proposed algorithm improve the accuracy of spatial QRS-T angle estimation compared to the standard approach in the presence of ECG delineation errors?
Does the proposed algorithm improve the accuracy of spatial QRS-T angle estimation compared to the standard approach in the presence of ECG delineation errors?
The proposed algorithm provides robust spatial QRS-T angle measurements even with inaccurate VCG annotation, facilitating its use in large datasets for risk prediction.
May facilitate robust QRS-T angle use in large ECG datasets; leaves open prospective validation before clinical adoption.
The spatial QRS-T angle (QRS-Ta) derived from the vectorcardiogram (VCG) is a strong risk predictor for ventricular arrhythmia and sudden cardiac death with potential use for mass screening. Accurate QRS-Ta estimation in the presence of ECG delineation errors is crucial for its deployment as a prognostic test. Our study assessed the effect of inaccurate QRS and T-wave marker placement on QRS-Ta estimation and proposes a robust method for its calculation. Reference QRS-Ta measurements were derived from 1,512 VCGs manually annotated by three expert reviewers. We systematically changed onset and offset timings of QRS and T-wave markers to simulate inaccurate placement. The QRS-Ta was recalculated using a standard approach and our proposed algorithm, which limits the impact of VCG marker inaccuracies by defining the vector origin as an interval preceding QRS-onset and redefines the beginning and end of QRS and T-wave loops. Using the standard approach, mean absolute errors (MAE) in peak QRS-Ta were >40% and sensitivity and precision in the detection of abnormality (>105°) were <80% and <65% respectively, when QRS-onset was delayed or QRS-offset anticipated >15 ms. Using our proposed algorithm, MAE for peak QRS-Ta were reduced to <4% and sensitivity and precision of abnormality were >94% for inaccuracies up to ±15 ms. Similar results were obtained for mean QRS-Ta. In conclusion, inaccuracies of QRS and T-wave markers can significantly influence the QRS-Ta. Our proposed algorithm provides robust QRS-Ta measurements in the presence of inaccurate VCG annotation, enabling its use in large datasets.
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Young et al. (2020) studied this question. Proposed algorithm for QRS-Ta calculation vs. Standard approach was evaluated on Mean absolute errors (MAE) in peak QRS-Ta and sensitivity/precision in detection of abnormality. The proposed algorithm for calculating the spatial QRS-T angle reduced mean absolute errors to <4% and improved sensitivity and precision to >94% compared to the standard approach.
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