比较 CEBS 数据库中 ECG 和 SCG 信号得出的 HRV 指数。
Comparison of HRV indices obtained from ECG and SCG signals from CEBS database.
机构信息
Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelt's Street, 41-800, Zabrze, Poland.
Katowice School of Technology, 43 Rolna Street, 40-055, Katowice, Poland.
出版信息
Biomed Eng Online. 2019 Jun 1;18(1):69. doi: 10.1186/s12938-019-0687-5.
BACKGROUND
Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals.
METHODS
We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features.
RESULTS
Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals.
CONCLUSIONS
Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.
背景
心率变异性（HRV）已成为评估心脏和自主神经系统功能的有用工具。近年来，人们对无电极心率监测产生了兴趣。心震图（SCG）是一种使用加速度计记录和分析心脏产生的振动的非侵入性技术。在这项研究中，我们比较了从心电图（ECG）、呼吸和心震图（CEBS）联合测量中获得的 HRV 指数，并确定了心率检测器对 SCG 信号的影响。
方法
我们考虑了两种 SCG 信号的心率检测器：使用 ECG 信号中的 R 波作为参考检测器来检测 SCG 中的心跳，以及仅使用 SCG 信号的心率检测器。我们进行了 HRV 分析并计算了时间和频率特征。
结果
尽管在噪声信号上的性能较低，但在所有 SCG 信号上，测试算法的心率检测性能都相当好，共检测到 85954 个心跳([Formula: see text]，[Formula: see text])。计算了 HRV 指数之间的相关性，作为决定线性模型拟合度的确定系数([Formula: see text])。获得了最高的[Formula: see text]值的是平均心搏间期([Formula: see text]对于参考算法，[Formula: see text]在最差情况下)，[Formula: see text]和[Formula: see text]([Formula: see text]对于最佳情况，[Formula: see text]对于最差情况)，而获得最低的是 pNN50 标准差([Formula: see text]在最差情况下)。使用稳健模型改善了从 ECG 和 SCG 信号获得的 HRV 指数之间的相关性，除了信号 p001-p020 中的 pNN50 值的[Formula: see text]值和所有分析信号。
结论
使用两种分析的心率检测器，从 ECG 和 SCG 计算得出的 HRV 指数相似，除了 SDNN、RMSSD、NN50、pNN50 和 pNN50 标准差([Formula: see text])。从 ECG 和 SCG 获得的 HRV 指数之间的关系受到 SCG 信号上使用的心率检测方法的影响。
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