Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Signal Processing, Computer-Assisted*; Alpha Rhythm/*physiology ; Cardiac Catheterization/*psychology ; Heart/*physiopathology ; Stress, Psychological/*physiopathology; Demography ; Electrocardiography ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Myocardial Revascularization ; Neurotransmitter Agents/blood ; Regression Analysis ; Stress, Psychological/blood

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

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0 (Neurotransmitter Agents)

Date Created: 20150820 Date Completed: 20160809 Latest Revision: 20181113

20221213

PMC4541158

10.1038/srep13315

26286628