Cardiology Service, Arrhythmia Unit, Hospital General Universitario Virgen de la Arrixaca, El Palmar, Murcia 30120, Spain.
Department of Signal Theory and Communications, University of de Alcalá, Alcalá de Henares, Madrid 28805, Spain.
Sensors (Basel). 2017 Oct 25;17(11):2448. doi: 10.3390/s17112448.
Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters.
噪声和伪像是固有的污染成分，在动态心电图（Holter ECG）监测中尤其存在。在长期监测（LTM）记录中，噪声的存在更为显著，因为这些记录是在患者进行日常活动后连续几天收集的；因此，强烈的伪影成分可能会暂时影响 LTM 记录的临床测量。传统上，通过几种定量信号指标（如信噪比（SNR））来处理噪声的存在问题，将其视为不需要的成分去除，但当前系统并未提供有关噪声对 ECG 临床评估的真实影响的任何信息。作为对经典方法的替代方法的第一步，这项工作评估了 ECG 的质量，假设当 ECG 具有临床可解释性时，它具有良好的质量。因此，我们的假设是：(a) 可以为噪声对 ECG 的影响创建临床严重程度评分；(b) 以其时间和统计分布的一致性来描述其特征；(c) 在 LTM 场景中使用它进行信号质量评估。为此，从临床角度组装和标记了外部事件记录器（EER）信号的数据库，将其用作噪声严重程度分类的金标准。这些设备被认为可以捕获在长时间内更容易受到噪声干扰的那些信号段。然后，通过将这些临床严重程度标准与传统噪声消除方法中获取的常规定量指标进行比较，对 ECG 噪声进行了特征描述，并提出了噪声图作为实现这一比较的新表示工具。我们的结果表明，所评估的基准定量噪声测量标准均不能准确地估计噪声的临床严重程度。报告了一项长期 ECG 的案例研究，显示了与用于创建临床噪声金标准的 EER 信号在统计和时间方面的对应关系和特性。所提出的噪声图以及噪声临床严重程度特征的统计一致性，为即将推出的系统铺平了道路，这些系统可以为我们提供噪声临床严重程度的噪声图，从而使用户能够使用不同的技术和不同的测量临床参数来处理不同的 ECG 段。
