Echeverría J C, Crowe J A, Woolfson M S, Hayes-Gill B R
School of Electrical & Electronic Engineering, University of Nottingham, UK.
Med Biol Eng Comput. 2001 Jul;39(4):471-9. doi: 10.1007/BF02345370.
The analysis of heart rate variability, involving changes in the autonomic modulation conditions, demands specific capabilities not provided by either parametric or non-parametric spectral estimation methods. Moreover, these methods produce time-averaged power estimates over the entire length of the record. Recently, empirical mode decomposition and the associated Hilbert spectra have been proposed for non-linear and non-stationary time series. The application of these techniques to real and simulated short-term heart rate variability data under stationary and non-stationary conditions is presented. The results demonstrate the ability of empirical mode decomposition to isolate the two main components of one chirp series and three signals simulated by the integral pulse frequency modulation model, and consistently to isolate at least four main components localised in the autonomic bands of 14 real signals under controlled breathing manoeuvres. In addition, within the short time-frequency range that is recognised for heart rate variability phenomena, the Hilbert amplitude component ratio and the instantaneous frequency representation are assessed for their suitability and accuracy in time-tracking changes in amplitude and frequency in the presence of non-stationary and non-linear conditions. The frequency tracking error is found to be less than 0.22% for two simulated signals and one chirp series.
心率变异性分析涉及自主神经调节状况的变化，需要参数或非参数谱估计方法所不具备的特定能力。此外，这些方法会在记录的整个时长内生成时间平均功率估计值。最近，经验模式分解及相关的希尔伯特谱已被用于非线性和非平稳时间序列。本文介绍了这些技术在平稳和非平稳条件下对真实和模拟的短期心率变异性数据的应用。结果表明，经验模式分解能够分离一个线性调频信号序列的两个主要成分以及由积分脉冲频率调制模型模拟的三个信号，并在控制呼吸操作下始终能分离出14个真实信号自主神经频段内的至少四个主要成分。此外，在心率变异性现象所公认的短时间频率范围内，评估了希尔伯特幅度分量比和瞬时频率表示在存在非平稳和非线性条件时跟踪幅度和频率随时间变化的适用性和准确性。对于两个模拟信号和一个线性调频信号序列，发现频率跟踪误差小于0.22%。
