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An Efficient Time-Varying Filter for Detrending and Bandwidth Limiting the Heart Rate Variability Tachogram without Resampling: MATLAB Open-Source Code and Internet Web-Based Implementation

机译:一个有效的时变过滤器用于不进行重采样的趋势和带宽限制心率变异性速度图:MATLAB开源代码和基于Internet网络的实现

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摘要

The heart rate variability (HRV) signal derived from the ECG is a beat-to-beat record of RR intervals and is, as a time series, irregularly sampled. It is common engineering practice to resample this record, typically at 4 Hz, onto a regular time axis for analysis in advance of time domain filtering and spectral analysis based on the DFT. However, it is recognised that resampling introduces noise and frequency bias. The present work describes the implementation of a time-varying filter using a smoothing priors approach based on a Gaussian process model, which does not require data to be regular in time. Its output is directly compatible with the Lomb-Scargle algorithm for power density estimation. A web-based demonstration is available over the Internet for exemplar data. The MATLAB (MathWorks Inc.) code can be downloaded as open source.
机译:从ECG导出的心率变异性(HRV)信号是RR间隔的逐次记录,并且作为时间序列不规则地采样。在DFT基础上进行时域滤波和频谱分析之前,通常的工程实践是将此记录(通常在4Hz处)重采样到规则的时间轴上,以进行分析。但是,已经认识到重采样会引入噪声和频率偏差。本工作描述了基于高斯过程模型的使用平滑先验方法的时变滤波器的实现,该过程不需要数据在时间上是规则的。其输出与用于功率密度估计的Lomb-Scargle算法直接兼容。 Internet上可以获取基于Web的示例数据演示。 MATLAB(MathWorks Inc.)代码可以作为开放源代码下载。

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