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

Hung Chih Chiu, Yen Hung Lin, Men Tzung Lo, Sung Chun Tang, Tzung Dau Wang, Hung Chun Lu, Yi Lwun Ho, Hsi Pin Ma, Chung Kang Peng

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish
Article number13315
JournalScientific Reports
Volume5
DOIs
StatePublished - 19 Aug 2015

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