Recurrence Quantification Analysis of Cardiovascular System During Cardiopulmonary Resuscitation

Author:

Chen Shuxin,Jiang Lijun,Pan Chang,Pang Jiaojiao,Xu Feng,Wang Jiali,Chen Yuguo,Li Ke

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Mozaffarian, D., Benjamin, E.J., Go, A.S., Arnett, D.K.: Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circ. (New York, N.Y.) 133(4), e38–e360 (2016)

2. Rea, T.D., Page, R.L.: Community approaches to improve resuscitation after out-of-hospital sudden cardiac arrest. Circ. (New York, N.Y.) 121(9), 1134–1140 (2010)

3. Perkins, G.D., Jacobs, I.G., Nadkarni, V.M., Berg, R.A.: Cardiac arrest and cardiopulmonary resuscitation outcome reports: update of the utstein resuscitation registry templates for out-of-hospital cardiac arrest a statement for healthcare professionals from a task force of the international liaison committee on resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia). Circ. (New York, N.Y.) 132(13), 1286–1300 (2015)

4. Acharya, U.R., Fujita, H., Sudarshan, V.K., Ghista, D.N.: Automated prediction of sudden cardiac death risk using Kolmogorov complexity and recurrence quantification analysis features extracted from HRV signals, pp. 1110–1115. IEEE (2015)

5. Chae, M., Gil, H.-W., Cho, N.-J., Lee, H.: Machine learning-based cardiac arrest prediction for early warning system. Math. (Basel) 10(12), 2049 (2022)

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