Robust aortic valve non-opening detection for different cardiac conditions

Hui Lee Ooi, Siew Cheok Ng*, Einly Lim, Robert F. Salamonsen, Alberto P. Avolio, Nigel H. Lovell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

In recent years, extensive studies have been conducted in the area of pumping state detection for implantable rotary blood pumps. However, limited studies have focused on automatically identifying the aortic valve non-opening (ANO) state despite its importance in the development of control algorithms aiming for myocardial recovery. In the present study, we investigated the performance of 14 ANO indices derived from the pump speed waveform using four different types of classifiers, including linear discriminant analysis, logistic regression, back propagation neural network, and k-nearest neighbors (KNN). Experimental measurements from four greyhounds, which take into consideration the variations in cardiac contractility, systemic vascular resistance, and total blood volume were used. By having only two indices,(i) the root mean square value, and (ii) the standard deviation, we were able to achieve an accuracy of 92.8% with the KNN classifier. Further increase of the number of indices to five for the KNN classifier increases the overall accuracy to 94.6%.

Original languageEnglish
Pages (from-to)E57-E67
Number of pages11
JournalArtificial Organs
Volume38
Issue number3
DOIs
Publication statusPublished - Mar 2014

Fingerprint

Dive into the research topics of 'Robust aortic valve non-opening detection for different cardiac conditions'. Together they form a unique fingerprint.

Cite this