Analysis of Electrocardio Signal Based on Approximate Entropy

Abstract:

The research and detection of heart disease depends on the analysis of the characteristic of electrocardio signal. Current analysis methods mainly include: (1) time domain analysis is a common used approach. With experience learned by observation and calculation, researchers examine errors and interferences to calculate means and variances directly within time domain. Analysis quality of this method demands higher request for researchers’ experience and skill although it’s a direct and significant result. (2) Frequency domain analysis, such as spectrum estimation, is largely applied to electrocardio signal researches and clinical applications. The analysis reflects abundant electrocardio activities, but failed to show details of the characteristics due to lack of time information. (3) time-frequency domain analysis describes energy density under different time and frequency of electrocardio signal at one time. It clarifies the relationship of signal frequency’s changing along with time such as wavelet transform method. (4) Nonlinear analysis is generally applied to biomedicine signal research in recent years. Correlation dimension, kolmogorov entropy, lyapunov component are major research methods to estimate some nonlinear dynamic parameters to represent the characteristic of electrocardio signal.

• Periodical: Advanced Materials Research (Volume 429)
• Pages: 195-199
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.429.195
• Online since: January 2012
• Authors: Xiao Lei Zhao, Ming Rong Ren, Ya Ting Zhang, Pu Wang
• Keywords: Approximate Entropy, Nonlinear, Ventricular Fibrillation, Ventricular Tachycardia

References

[1] Pincus SM. Approximate entropy as a measure of system complexity[M]. Proc Natl Acad Sci USA，1991，88：2297-2301.

DOI: 10.1073/pnas.88.6.2297

[2] Yang Fusheng, Liao Wancai. Approximate entropy: a complexity measure suitable for short data, vol. 21. Tsinghua University: Beijing, 1999, p.283–286.

[3] Gu Huaguang , Ren Wei , L iu Gang , Shen Xianyun , Meng Jingrui. Approximate Entropy and its Application in Heart Rate Variability Analysis . Space Medicine & Medical Engineering , vol. 13. 2000, pp.: 417-421.

[4] H.S. Kim, R. Eykholt, J.D. Salas. Nonlinear dynamics, delay times, and embedding windows. Physica D. 1999, 127: 48~60.

DOI: 10.1016/s0167-2789(98)00240-1

[5] Ki H. Chon, Christopher G. Scully, Sheng Lu. Approximate entropy for all signals. IEEE Engineering in medicine and biology magazine. vol. 9. 2009, pp.18-23.

DOI: 10.1109/memb.2009.934629

[6] Yasunari YokotaAn. Approximate Method for Bayesian Entropy Estimation for a Discrete Random Variable. Proceedings of the 26th Annual International Conference of the IEEE EMBS. USA. 2004：99-102.

DOI: 10.1109/iembs.2004.1403100