用于HR和HRV估计的地震心电图希尔伯特振动分解

Moirangthem James Singh, L. Sharma, S. Dandapat
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引用次数: 2

摘要

提出了一种基于希尔伯特振动分解(HVD)的时变心率估计方法。心率(HR)估计方法包括利用HVD算法对信号进行分解,生成心率包络线,并从平滑包络线中检测峰值以计算心跳间隔。我们从搏动间隔中推导出心率变异性(HRV)指标。该方法不需要参考心电信号。同样的信号也经受了经验模态分解(EMD)和变分模态分解(VMD)方法。为了比较这三种分解方法,我们使用了来自Physionet Archive的CEBS数据库进行测试和验证。结果表明,使用HVD方法估算节拍间隔的准确性比其他方法更高,并且使用我们的方法可以准确地推导出HRV指标。性能结果表明,标准心电图得出的心跳和HRV指标与scg得出的心跳和HRV指标在健康受试者中具有可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hilbert Vibration Decomposition of Seismocardiogram for HR and HRV Estimation
This paper presents a new time-varying decomposition method based on the Hilbert Vibration Decomposition (HVD) for estimating heart rate from a Seismocardiogram (SCG). The heart rate (HR) estimation method consists of signal decomposition using the HVD algorithm, heart rate envelope generation, and peak detection from the smooth envelope for beat-to-beat interval calculation. We derived the heart rate variability (HRV) metrics from the interbeat intervals. The method doesn’t require a reference ECG signal. The same signals are also subjected to Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) methods. To compare these three decomposition methods, the CEBS database from Physionet Archive was used for testing and validation. The results show better accuracy in beat-to-beat interval estimation using the HVD method than others, and HRV metrics are accurately derived using our methodology. The performance results demonstrate that the standard ECG-derived heartbeats and HRV metrics and SCG-derived heartbeats and HRV metrics are comparable for healthy subjects.
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