Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
PLoS One. 2017 Apr 12;12(4):e0175087. doi: 10.1371/journal.pone.0175087. eCollection 2017.
Increased variability of beat-to-beat QT-interval durations on the electrocardiogram (ECG) has been associated with increased risk for fatal and non-fatal cardiac events. However, techniques for the measurement of QT variability (QTV) have not been validated since a gold standard is not available. In this study, we propose a validation method and illustrate its use for the validation of two automatic QTV measurement techniques.
Our method generates artificial standard 12-lead ECGs based on the averaged P-QRS-T complexes from a variety of existing ECG signals, with simulated intrinsic (QT interval) and extrinsic (noise, baseline wander, signal length) variations. We quantified QTV by a commonly used measure, short-term QT variability (STV). Using 28,800 simulated ECGs, we assessed the performance of a conventional QTV measurement algorithm, resembling a manual QTV measurement approach, and a more advanced algorithm based on fiducial segment averaging (FSA).
The results for the conventional algorithm show considerable median absolute differences between the simulated and estimated STV. For the highest noise level, median differences were 4-6 ms in the absence of QTV. Increasing signal length generally yields more accurate STV estimates, but the difference in performance between 30 or 60 beats is small. The FSA algorithm proved to be very accurate, with most median absolute differences less than 0.5 ms, even for the highest levels of disturbance.
Artificially constructed ECGs with a variety of disturbances allow validation of QTV measurement procedures. The FSA algorithm provides highly accurate STV estimates under varying signal conditions, and performs much better than traditional beat-by-beat analysis. The fully automatic operation of the FSA algorithm enables STV measurement in large sets of ECGs.
心电图（ECG）上逐搏 QT 间期时长的变异性增加与致命和非致命性心脏事件的风险增加有关。然而，由于缺乏金标准，尚未对 QT 变异性（QTV）的测量技术进行验证。在本研究中，我们提出了一种验证方法，并举例说明了该方法在两种自动 QTV 测量技术的验证中的应用。
我们的方法基于来自各种现有 ECG 信号的平均 P-QRS-T 复合体生成人工标准 12 导联 ECG，模拟固有（QT 间期）和外在（噪声、基线漂移、信号长度）变化。我们使用一种常用的测量方法，即短期 QT 变异性（STV）来量化 QTV。使用 28800 个模拟 ECG，我们评估了一种类似于手动 QTV 测量方法的常规 QTV 测量算法和一种基于基准段平均（FSA）的更先进算法的性能。
常规算法的结果显示，模拟和估计的 STV 之间存在相当大的中位数绝对差异。在没有 QTV 的情况下，最高噪声水平下的中位数差异为 4-6ms。增加信号长度通常会产生更准确的 STV 估计，但 30 或 60 个节拍之间的性能差异较小。FSA 算法被证明非常准确，即使在干扰水平最高的情况下，大多数中位数绝对差异也小于 0.5ms。
具有各种干扰的人工构建 ECG 可用于验证 QTV 测量程序。FSA 算法在各种信号条件下提供高度准确的 STV 估计，并且比传统的逐搏分析表现要好得多。FSA 算法的全自动操作可实现 ECG 大组的 STV 测量。
