修改的多尺度雷尼分布对于短期心率波动性分析
Manhong Shi1, Yinuo Shi2, Yuxin Lin3,4
1College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, 233000, China. shimh@ahstu.edu.cn.
BMC medical informatics and decision making
|November 20, 2024
概括
一种新的方法,修改的多尺度雷尼分布 (MMRDis),增强了对短时间序列的复杂性分析. 它提供稳定可靠的测量,特别是心率变化 (HRV) 信号,有助于心血管疾病查.
科学领域:
- 生理信号分析分析生理信号分析
- 复杂度指标 复杂度指标 复杂度指标
- 时间序列分析时间序列分析.
背景情况:
- 多尺度样本 (MSE) 被广泛用于时间序列的复杂性,但由于后续可比性降低和在更高的尺度上未定义的值,它对短期数据有困难.
- 短时间序列分析受到减少样本 (SampEn) 的可靠性和在MSE中增加尺度的潜在未定义值的挑战.
- 引入了一种新的修改的多尺度Renyi分布 (MMRDis) 方法,以解决短时间序列的现有复杂度指标的局限性.
研究的目的:
- 引入和验证修改的多尺度Renyi分布 (MMRDis) 作为短时间序列的强大的复杂度指标.
- 评估MMRDis的计算稳定性和可靠性,特别是对于心率变化 (HRV) 等生理信号.
- 评估MMRDis在区分健康和病态生理/病理信号方面的能力.
主要方法:
- MMRDis采用移动平均程序来生成一个时间序列家族,在多个时间尺度上捕捉动态行为.
- 该MMRDis是计算的原始和粗粒度的时间序列,从移动平均过程中得出的.
- 该方法在模拟噪音时间序列和来自不同年龄和健康群体的短期心率变化 (HRV) 信号上进行了测试.
主要成果:
- 通过模拟高斯白噪声和1/f噪声,MMRDis显示出卓越的计算稳定性,避免在短时间序列中进行未定义的测量.
- 来自MMRDis的复杂度值随着年龄和疾病的增长而降低,来自老年人,年轻人和患者组的短期HRV信号.
- 与最近的其他复杂度指标相比,MMRDis显示出对短期HRV生理/病理信号的优异区分能力.
结论:
- MMRDis为短时间序列提供了稳定可靠的复杂度测量,克服了传统方法 (如MSE) 的局限性.
- 该MMRDis方法有效分析短期心率变化 (HRV) 信号,显示衰老和疾病的复杂性降低.
- MMRDis是快速查心血管疾病的一个有前途的工具.
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