Abstract
Heart rate variability (HRV) can be used to predict, prevent heart related diseases and do prognosis evaluation. A method was proposed based on Poincaré plot and symbolic dynamics to analyze ECG feature. Firstly, HRV sequence was extracted from the ECG signal, and presented in Poincaré plot. Then, the splashes in different areas of the plot would be numbered and coded. The entropy of the ECG signal, calculated by the probability of each code, was applied to recognize and classify ECG signal as feature. The experiment results show that, the accuracy rates of classification in normal sinus rhythm and atrial fibrillation, normal sinus rhythm and premature beat are 86.67%, 90% respectively, the method can distinguish normal sinus rhythm from arrhythmia effectively.
Original language | English |
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Pages (from-to) | 1084-1089 |
Number of pages | 6 |
Journal | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
Volume | 37 |
Issue number | 10 |
DOIs | |
Publication status | Published - 1 Oct 2017 |
Keywords
- HRV
- Poincaré plot
- Shannon entropy
- Symbolic dynamics