Interdisciplinary Program in Medical and Biological Engineering, Seoul National University, Korea.
Physiol Meas. 2009 Oct;30(10):1039-50. doi: 10.1088/0967-3334/30/10/005. Epub 2009 Aug 28.
In this study, optimal methods for re-sampling and spectral estimation in frequency-domain heart rate variability (HRV) analysis were investigated through a simulation using artificial RR-interval data. Nearest-neighbour, linear, cubic spline and piecewise cubic Hermite interpolation methods were considered for re-sampling and representative non-parametric, parametric, and uneven approaches were used for spectral estimation. Based on this result, the effects of missing RR-interval data on frequency-domain HRV analysis were observed through the simulation of missing data using real RR-interval tachograms. For this simulation, data including the simulated artefact section (0-100 s) were used; these data were selected randomly from the real RR data obtained from the MIT-BIH normal sinus rhythm RR-interval database. In all, 7182 tachograms of 5 min durations were used for this analysis. The analysis for certain missing data durations is performed by 100 Monte Carlo runs. TF, VLF, LF and HF were estimated as the frequency-domain parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters were calculated. Rules obtained from the results of these simulations were evaluated with real missing RR-interval data derived from a capacitive-coupled ECG during sleep.
在这项研究中，通过使用人工 RR 间期数据进行模拟，研究了在频域心率变异性 (HRV) 分析中重新采样和谱估计的最佳方法。考虑了最近邻、线性、三次样条和分段三次埃尔米特插值方法进行重采样，并使用了代表性的非参数、参数和不均匀方法进行谱估计。基于这一结果，通过使用真实 RR 间隔心动图模拟缺失数据，观察了缺失 RR 间隔数据对频域 HRV 分析的影响。对于这种模拟，使用包括模拟伪影部分 (0-100 秒) 的数据;这些数据是从麻省理工学院-贝斯以色列女执事医疗中心正常窦性节律 RR 间隔数据库中获得的真实 RR 数据中随机选择的。总共对 7182 个 5 分钟时长的心动图进行了分析。通过 100 次蒙特卡罗运行对某些缺失数据持续时间进行分析。在每次运行中，将 TF、VLF、LF 和 HF 估计为频域参数，并计算这些参数的有缺失数据持续时间和无缺失数据持续时间的数据之间的归一化误差。从这些模拟结果中获得的规则用睡眠期间电容耦合 ECG 产生的真实缺失 RR 间隔数据进行了评估。
