Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk

Ryo Matsuoka, Kohzoh Yoshino, Eiichi Watanabe, Ken Kiyono

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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].

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781538654880
DOIs
Publication statusPublished - 21-01-2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 03-12-201806-12-2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period03-12-1806-12-18

Fingerprint

Entropy
Atrial Fibrillation
Heart Rate
Stroke
Probability density function
Risk assessment
Probability distributions
Time series

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

Cite this

Matsuoka, R., Yoshino, K., Watanabe, E., & Kiyono, K. (2019). Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, ... L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 [8621178] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621178
Matsuoka, Ryo ; Yoshino, Kohzoh ; Watanabe, Eiichi ; Kiyono, Ken. / Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).
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title = "Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk",
abstract = "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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Matsuoka, R, Yoshino, K, Watanabe, E & Kiyono, K 2019, Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621178, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 03-12-18. https://doi.org/10.1109/BIBM.2018.8621178

Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk. / Matsuoka, Ryo; Yoshino, Kohzoh; Watanabe, Eiichi; Kiyono, Ken.

Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. 8621178 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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

PY - 2019/1/21

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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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M3 - Conference contribution

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BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

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A2 - Wang, Haiying

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PB - Institute of Electrical and Electronics Engineers Inc.

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Matsuoka R, Yoshino K, Watanabe E, Kiyono K. Analysis of multiscale entropy characteristics of heart rate variability in patients with permanent atrial fibrillation for predicting ischemic stroke risk. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8621178. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621178