TY - GEN
T1 - Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk
AU - Matsuoka, Ryo
AU - Yoshino, Kohzoh
AU - Watanabe, Eiichi
AU - Kiyono, Ken
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - It has been reported that the complexity characteristics of heart rate variability (HRV) in patients with permanent atrial fibrillation (AFib) based on multiscale entropy (MSE) analysis are associated with ischemic stroke risk. However, the interpretation of HRV complexity is not clear and the mathematical and physical relationships between HRV and ischemic stroke have not been established. MSE is determined not only by the correlation characteristics but also by probability density function characteristics. The aim of this study was to clarify which characteristics were important for the association between MSE and ischemic stroke risk in patients with permanent AFib. We analyzed 24 hours of HRV data from 173 patients with permanent AFib. Results show that long-range correlations like 1/f fluctuations in a range greater than 90s were observed in HRV time series in patients with AFib, but that these values had no predictive power as an ischemic stroke risk factor. On the other hand, probability density functions of coarse-grained scales greater than 2s were significantly associated with ischemic stroke risk. These results suggest that probability density functions are a useful risk factor for improving ischemic stroke risk assessment. To investigate the probability density function characteristics more in detail, we analyzed the asymmetric non-Gaussian properties of the probability distribution of HRV data. Part of this study was published in the journal Entropy [1].
AB - It has been reported that the complexity characteristics of heart rate variability (HRV) in patients with permanent atrial fibrillation (AFib) based on multiscale entropy (MSE) analysis are associated with ischemic stroke risk. However, the interpretation of HRV complexity is not clear and the mathematical and physical relationships between HRV and ischemic stroke have not been established. MSE is determined not only by the correlation characteristics but also by probability density function characteristics. The aim of this study was to clarify which characteristics were important for the association between MSE and ischemic stroke risk in patients with permanent AFib. We analyzed 24 hours of HRV data from 173 patients with permanent AFib. Results show that long-range correlations like 1/f fluctuations in a range greater than 90s were observed in HRV time series in patients with AFib, but that these values had no predictive power as an ischemic stroke risk factor. On the other hand, probability density functions of coarse-grained scales greater than 2s were significantly associated with ischemic stroke risk. These results suggest that probability density functions are a useful risk factor for improving ischemic stroke risk assessment. To investigate the probability density function characteristics more in detail, we analyzed the asymmetric non-Gaussian properties of the probability distribution of HRV data. Part of this study was published in the journal Entropy [1].
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U2 - 10.1109/BIBM.2018.8621178
DO - 10.1109/BIBM.2018.8621178
M3 - Conference contribution
AN - SCOPUS:85062539068
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 2785
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -