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- W4366400906 abstract "Abstract Background: 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, the application of MSE will not be satisfactory due to its inaccurate or mathematics constraints for a short-term time series as the scale factor increases. To overcome the shortcoming, a modified multiscale Renyi distribution entropy (MMRDis) was proposed in our present work Methods: 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. Results: We showed the MMRDis method on simulated Gaussian white and 1/f noise time series, indicating that MMRDis had a superior computational stability and can avoid the occurrence of undefined measurement for the short-term time series. Besides, the short-term heart rate variability (HRV) signals extracted from healthy elderly, healthy young people and subjects with congestive heart failure and atrial fibrillation were used to evaluate the performance of MMRDis. The results showed that the complexity measurement values of MMRDis decreased with aging and disease and had the better distinction capability for the short-term HRV physiology/pathological signals than those of several recently proposed complexity metrics, Conclusions: MMRDis was a promising measurement for screening cardiovascular condition within a short time." @default.
- W4366400906 created "2023-04-21" @default.
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- W4366400906 date "2023-04-19" @default.
- W4366400906 modified "2023-10-18" @default.
- W4366400906 title "Modified Multiscale Renyi Distribution Entropy for Short-Term Heart Rate Variability Analysis" @default.
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- W4366400906 doi "https://doi.org/10.21203/rs.3.rs-2815317/v1" @default.
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