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.
复制标题
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
中科院分区:
文献类型:
--
作者:
Chung YM;Hu CS;Lo YL;Wu HT
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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影响因子:
8
作者:
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通讯作者:
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DOI:
10.1109/taes.2016.160405
发表时间:
2016-12-01
影响因子:
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作者:
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通讯作者:
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DOI:
10.1016/s0921-884x(96)96070-1
发表时间:
1997-05-01
期刊:
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子:
--
作者:
Bonnet, MH;Arand, DL
通讯作者:
Arand, DL
影响因子:
4
作者:
Billman GE
通讯作者:
Billman GE