A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification.

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification.
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心率变异性分析的持续同源方法及其在睡眠-觉醒分类中的应用。

DOI:
10.3389/fphys.2021.637684
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发表时间:
2021
影响因子:
4
通讯作者:
Wu HT
Wu HT
中科院分区:
医学2区
文献类型:
--
作者:
Chung YM;Hu CS;Lo YL;Wu HT

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持久同调理论是近年来在代数拓扑学领域发展起来的一种研究数据集形状的理论。它是一种有效的数据分析工具,对噪声具有很强的鲁棒性,已得到广泛的应用。我们展示了一个通用的管道,应用持久同源性研究时间序列,特别是瞬时心率时间序列的心率变异性(HRV)分析。第一步是从两个不同的方面捕获时间序列的形状-持续的同源性,因此持续图的子水平集和Taken的滞后图。其次,我们提出了一个系统的和计算效率高的方法来总结持久性图,我们创造了持久性统计。为了证明我们提出的方法,我们将这些工具应用于HRV分析和睡眠-觉醒,REM-NREM(快速眼球运动和非快速眼球运动)和睡眠-REM-NREM分类问题。该算法通过跨数据库验证方案在三个不同的数据集上进行评估。我们的方法的性能优于最先进的算法,并且在不同的数据集上结果是一致的。
Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.
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