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Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network | IEEE Journals & Magazine | IEEE Xplore

Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network


Abstract:

Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE ...Show More

Abstract:

Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network (DBN). The proposed prediction algorithm uses eight novel HRV signal features, which are calculated based on the following steps. First, the instantaneous amplitude (IA), instantaneous frequency (IF), and instantaneous phase (IP) are calculated for the HRV signals. Second, the high-order cumulant is estimated for the HRV and its IA, IF, and IP. Third, a high-order singular entropy is calculated to measure the fluctuation in signals. Fourth, eight novel features are obtained and processed using a DBN classifier designed for CCE prediction. The DBN classification method, with the novel HRV features, outperformed existing methods in terms of accuracy. Thus, the scheme proposed herein provided a novel direction for predicting CCEs.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 27, Issue: 4, April 2023)
Page(s): 1670 - 1680
Date of Publication: 29 March 2022

ISSN Information:

PubMed ID: 35349460

Funding Agency:


References

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