Acute Hypotensive Episodes Prediction Based on Non-linear Chaotic Analysis_Journal of Biomedical Engineering

Authors：

JIANGDazhi ¹ , LILiyu ¹ ,  PENGChenfeng ²

1. College of Engineering, Shantou University, Shantou 515063, China;
2. Interactive Entertainment Group, Tencent Technology (Shenzhen) Company Ltd., Shenzhen 518057, China;

Corresponding author：

PENGChenfeng, Email: chaneypeng@gmail.com

Keywords：

acute hypotensive episode; chaos; prediction

DOI：

10.7507/1001-5515.20150038

Video：

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In intensive care units (ICU), the occurrence of acute hypotensive episodes (AHE) is the key problem for the clinical research and it is meaningful for clinical care if we can use appropriate computational technologies to predict the AHE. In this study, based on the records of patients in ICU from the MIMICⅡclinical data, the chaos signal analysis method was applied to the time series of mean artery pressure, and then the patient's Lyapunov exponent curve was drawn ultimately. The research showed that a curve mutation appeared before AHE symptoms took place. This is powerful and clear basis for AHE determination. It is also expected that this study may offer a reference to research of AHE theory and clinical application.

Citation： JIANGDazhi, LILiyu, PENGChenfeng. Acute Hypotensive Episodes Prediction Based on Non-linear Chaotic Analysis. Journal of Biomedical Engineering, 2015, 32(1): 209-213. doi: 10.7507/1001-5515.20150038 Copy

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11.	龚祝平.混沌时间序列的平均周期计算方法[J].系统工程, 2010, 28(12): 111-113.
12.	张海龙, 闵富红, 王恩荣.关于Lyapunov指数计算方法的比较[J].南京师范大学学报:工程技术版, 2012, 12(1): 5-9.

1. MOODY G B, LEHMAN L H. Predicting acute hypotensive episodes: The 10th Annual PhysioNet/Computers in Cardiology Challenge[J]. Comput Cardiol, 2009, 36(5445351): 541-544.
2. CRESPO C, MCNAMES J, ABOY M, et al. Precursors in the arterial blood pressure signal to episodes of acute hypotension in sepsis[C]//Proceedings of the 16th International EURASIP Conference BIOSIGNAL2002. Brno, Czech Republic: 2002, 16: 206-208.
3. FOURNIER P A, ROY J F. Acute hypotension episode prediction using information divergence for feature selection, and non-parametric methods for classification[C]//Computers in Cardiology, 2009. Park City, UT: 2009: 625-628.
4. HENRIQUES J H, ROCHA T R. Prediction of acute hypotensive episodes using neural network multi-models[C]//Computers in Cardiology, 2009. Park City, UT: 2009, 36: 549-552.
5. 赖丽娟, 王志刚, 吴效明, 等.基于支持向量机的ICU中急性低血压预测模型研究[J].生物医学工程学杂志, 2011, 28(3): 451-455.
6. 王志刚, 赖丽娟, 熊冬生, 等.基于AR模型和支持向量机的急性低血压预测[J].中国生物医学工程学报, 2011, 30(2): 250-255.
7. 赖丽娟, 王志刚, 吴效明.基于小波变换和支持向量机的急性低血压预测方法研究[J].透析与人工器官, 2011, 22(1): 28-33.
8. ADELI H, GHOSH-DASTIDAR S, DADMEHR N. A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy[J]. IEEE Trans Biomed Eng, 2007, 54(2): 205-211.
9. 吕金虎, 陆君安, 陈士华.混沌时间序列分析及其应用[M].武汉:武汉大学出版社, 2005.
10. 陆振波, 蔡志明, 姜可宇.基于改进的C-C方法的相空间重构参数选择[J].系统仿真学报, 2007, 19(11): 2527-2529, 2538.
11. 龚祝平.混沌时间序列的平均周期计算方法[J].系统工程, 2010, 28(12): 111-113.
12. 张海龙, 闵富红, 王恩荣.关于Lyapunov指数计算方法的比较[J].南京师范大学学报:工程技术版, 2012, 12(1): 5-9.

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