Algorithm for the detection of congestive heart failure index

KANG, Yi Da, ZHUO, Deming, FOO, Rui En Anne, LIM, Choo Min, FAUST, Oliver and HAGIWARA, Yuki (2017). Algorithm for the detection of congestive heart failure index. Journal of Mechanics in Medicine and Biology, 17 (7), p. 1740043.

Full text not available from this repository.
Official URL:
Link to published version::
Related URLs:


his study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low pp-values and high feature ranking criterion scores.

Item Type: Article
Additional Information: ** From Crossref via Jisc Publications Router.
Uncontrolled Keywords: Biomedical Engineering
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number:
Page Range: p. 1740043
SWORD Depositor: Hilary Ridgway
Depositing User: Hilary Ridgway
Date Deposited: 20 Oct 2017 09:11
Last Modified: 18 Mar 2021 17:15

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics