College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, 233000, China.
School of Mathematics and Statistics, Hunan Normal University, Changsha, 410012, China.
BMC Med Inform Decis Mak. 2024 Nov 19;24(1):346. doi: 10.1186/s12911-024-02763-1.
Multiscale sample entropy (MSE) is a prevalent complexity metric to characterize a time series and has been extensively applied to the physiological signal analysis. However, for a short-term time series, the likelihood of identifying comparable subsequences decreases, leading to higher variability in the Sample Entropy (SampEn) calculation. Additionally, as the scale factor increases in the MSE calculation, the coarse-graining process further shortens the time series. Consequently, each newly generated time series at a larger scale consists of fewer data points, potentially resulting in unreliable or undefined entropy values, particularly at higher scales. To overcome the shortcoming, a modified multiscale Renyi distribution entropy (MMRDis) was proposed in our present work.
The MMRDis method uses a moving-averaging procedure to acquire a family of time series, each of which quantify the dynamic behaviors of the short-term time series over the multiple temporal scales. Then, MMRDis is constructed for the original and the coarse-grained time series.
The MMRDis method demonstrated superior computational stability on simulated Gaussian white and 1/f noise time series, effectively avoiding undefined measurements in short-term time series. Analysis of short-term heart rate variability (HRV) signals from healthy elderly individuals, healthy young people, and subjects with congestive heart failure and atrial fibrillation revealed that MMRDis complexity measurement values decreased with aging and disease. Additionally, MMRDis exhibited better distinction capability for short-term HRV physiological/pathological signals compared to several recently proposed complexity metrics.
MMRDis was a promising measurement for screening cardiovascular condition within a short time.
多尺度样本熵（MSE）是一种常用的复杂性度量方法，用于描述时间序列，并已广泛应用于生理信号分析。然而，对于短期时间序列，识别可比子序列的可能性降低，导致样本熵（SampEn）计算的变异性增加。此外，随着 MSE 计算中尺度因子的增加，粗粒化过程进一步缩短了时间序列。因此，在较大尺度下生成的每个新时间序列包含的点数较少，可能导致熵值不可靠或未定义，尤其是在较高的尺度下。为了克服这一缺点，我们在本研究中提出了一种改进的多尺度 Renyi 分布熵（MMRDis）方法。
MMRDis 方法使用移动平均过程获取一系列时间序列，每个时间序列都量化了短期时间序列在多个时间尺度上的动态行为。然后，对原始和粗粒化时间序列构建 MMRDis。
MMRDis 方法在模拟高斯白噪声和 1/f 噪声时间序列上表现出优越的计算稳定性，有效地避免了短期时间序列中未定义的测量。对健康老年人、健康年轻人以及充血性心力衰竭和心房颤动患者的短期心率变异性（HRV）信号进行分析表明，MMRDis 复杂性测量值随年龄增长和疾病而降低。此外，与最近提出的几种复杂性度量方法相比，MMRDis 对短期 HRV 生理/病理信号具有更好的区分能力。
MMRDis 是一种有前途的在短时间内筛选心血管状况的测量方法。
