School of Health and Social Care, University of Derby, Derby, UK.
Centre for Kidney Research and Innovation, University of Nottingham, Derby, UK.
J Healthc Eng. 2020 Dec 8;2020:8862074. doi: 10.1155/2020/8862074. eCollection 2020.
Short-term cardiovascular compensatory responses to perturbations in the circulatory system caused by haemodialysis can be investigated by the spectral analysis of heart rate variability, thus providing an important variable for categorising individual patients' response, leading to a more personalised treatment. This is typically accomplished by resampling the irregular heart rate to generate an equidistant time series prior to spectral analysis, but resampling can further distort the data series whose interpretation can already be compromised by the presence of artefacts. The Lomb-Scargle periodogram provides a more direct method of spectral analysis as this method is specifically designed for large, irregularly sampled, and noisy datasets such as those obtained in clinical settings. However, guidelines for preprocessing patient data have been established in combination with equidistant time-series methods and their validity when used in combination with the Lomb-Scargle approach is missing from literature. This paper examines the effect of common preprocessing methods on the Lomb-Scargle power spectral density estimate using both real and synthetic heart rate data and will show that many common techniques for identifying and editing suspect data points, particularly interpolation and replacement, will distort the resulting power spectrum potentially misleading clinical interpretations of the results. Other methods are proposed and evaluated for use with the Lomb-Scargle approach leading to the main finding that suspicious data points should be excluded rather than edited, and where required, denoising of the heart rate signal can be reliably accomplished by empirical mode decomposition. Some additional methods were found to be particularly helpful when used in conjunction with the Lomb-Scargle periodogram, such as the use of a false alarm probability metric to establish whether spectral estimates are valid and help automate the assessment of valid heart rate records, potentially leading to greater use of this powerful technique in a clinical setting.
通过心率变异性的频谱分析，可以研究血液透析引起的循环系统紊乱对短期心血管代偿反应，从而为个体患者的反应分类提供一个重要的变量，从而实现更个性化的治疗。这通常通过对不规则心率进行重采样来实现，以便在进行频谱分析之前生成等距时间序列，但重采样可能会进一步扭曲数据序列，而这些数据序列的解释已经可能因伪影的存在而受到影响。Lomb-Scargle 周期图提供了一种更直接的频谱分析方法，因为这种方法专门针对大型、不规则采样和嘈杂的数据集进行设计，例如在临床环境中获得的数据集。然而，与等距时间序列方法相结合的患者数据预处理指南已经建立，并且在与 Lomb-Scargle 方法结合使用时，其有效性在文献中缺失。本文使用真实和合成心率数据研究了常见预处理方法对 Lomb-Scargle 功率谱密度估计的影响，结果表明，许多用于识别和编辑可疑数据点的常用技术，特别是插值和替换，将扭曲生成的功率谱，从而可能导致对结果的临床解释产生误导。本文提出并评估了其他一些方法用于 Lomb-Scargle 方法，主要结论是可疑数据点应该被排除而不是编辑，如果需要，可以通过经验模态分解可靠地完成心率信号的去噪。一些额外的方法被发现与 Lomb-Scargle 周期图结合使用时特别有帮助，例如使用误报概率度量来确定频谱估计是否有效，并帮助自动评估有效心率记录，这可能会导致在临床环境中更广泛地使用这种强大的技术。
