Preprocessing effects in time-frequency distributions and spectral analysis of heart rate variability
Preprocessing effects in time-frequency distributions and spectral analysis of heart rate variability
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DOI：
10.1016/j.dsp.2008.09.004
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发表时间：
2009-07-01
影响因子：
2.9
通讯作者：
Colak, Omer H.
中科院分区：
文献类型：
--
作者：
Colak, Omer H.
心率变异性（HRV）是一种非常重要的无创性自主神经系统（ANS）分析工具。HRV信号包括缓慢变化的分量和快速变化的瞬态事件。本文研究了心率变异信号的预处理在时频分析和谱估计中的作用。预处理包括两个层次，即利用平滑先验方法去趋势和利用积分脉冲频率调制（IPFM）校正异位。本研究中使用的数据集来自自发性室性快速性心律失常（VTA）数据库。数据集包括至少1次室性快速性心律失常（VT）或室颤（VF）发作。研究了预处理对连续小波变换（CWT）和谱图时频分析以及周期图、Welch周期图和布尔格周期图时频分析的影响。分析了这些方法在确定VT或VF发作中的性能。通过对实验结果的比较，说明了预处理的重要性。(c)2008年爱思唯尔公司All rights reserved.
Heart rate variability (HRV) is very significance noninvasive tool for autonomic nervous system (ANS) analysis. HRV signal includes both slowly changing components and rapidly changing transient events. This study presents effects of preprocessing of HRV in time-frequency analysis and spectral estimations. Preprocessing includes two levels as detrending of trend using smoothness prior method and correction of ectopics using integral pulse frequency modulation (IPFM). The datasets used in this study are obtained from the Spontaneous Ventricular Tachyarrhythmia (VTA) database. Datasets include least one ventricular tachyarrhythmia (VT) or ventricular fibrillation (VF) episode. Effects of preprocessing are investigated for time-frequency analysis using continuous wavelet transform (CWT) and spectrogram and for spectral analysis using periodogram, Welch's periodogram and Burg's periodogram. Performance of these methods in determination of VT or VF episode is analyzed. Importance of preprocessing is explained comparing of obtained results. (c) 2008 Elsevier Inc. All rights reserved.