Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
IEEE Trans Biomed Eng. 2011 May;58(5):1356-64. doi: 10.1109/TBME.2010.2047859. Epub 2010 Apr 19.
We present a nonparametric adaptive surrogate test that allows for the differentiation of statistically significant T-wave alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise-induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data, no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data nonstationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise-rejection methods used with the spectral method (SM) and the modified moving average (MMA) techniques. Using a previously described realistic multilead model of TWA and real physiological noise, we demonstrate the proposed approach that reduces false TWA detections while maintaining a lower missed TWA detection, compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases: the Normal Sinus Rhythm Database, the Chronic Heart Failure Database, and the Sudden Cardiac Death Database. Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of HRs. The most-marked difference was generally found at higher HRs, and the new technique resulted in a larger margin of separability between patient populations than when the SM or MMA were applied to the same data.
我们提出了一种非参数自适应替代检验方法，可区分具有统计学意义的 T 波交替（TWA）与仅可通过噪声统计解释的交替模式。所提出的检验方法基于从原始 beat 序列的重复重新排列中获得的一组替代数据，估计 beat 序列中噪声诱导的交替模式的分布。因此，在评估数据中观察到的交替模式的显著性时，不假设潜在噪声分布。此外，由于在分析窗口内为每个 beat 序列分别计算了噪声诱导的交替幅度的分布，因此该方法对噪声和 TWA 中的数据非平稳性具有鲁棒性。所提出的用于拒绝噪声的替代方法与用于光谱方法（SM）和改进的移动平均（MMA）技术的标准噪声拒绝方法进行了比较。使用先前描述的 TWA 的真实多导联模型和真实生理噪声，与所有测试的其他方法相比，我们证明了所提出的方法可以减少假 TWA 检测，同时保持较低的 TWA 漏检率。简单的基于平均的 TWA 估计算法与替代显著性检验相结合，并在三个公共数据库上进行了评估：正常窦性节律数据库、慢性心力衰竭数据库和心脏性猝死数据库。在从 40 到 120 次/分钟（BPM）匹配心率（HR）间隔的每个数据库中评估 TWA 幅度的差异。使用双样本 Kolmogorov-Smirnov 检验，我们发现，在所有 HR 十年中，每个患者组之间的 TWA 水平存在显著差异。最显著的差异通常出现在较高的 HR 处，并且与将 SM 或 MMA 应用于相同数据时相比，新技术在患者人群之间产生了更大的可分离性。
