Handling Missing Strain (Rate) Curves Using K-Nearest Neighbor Imputation

被引：0

作者：

Tabassian, Mahdi ^{[1
,2
]}

Alessandrini, Martino ^{[1
,2
]}

Jasaityte, Ruta ^{[1
]}

De Marchi, Luca ^{[2
]}

Masetti, Guido ^{[2
]}

D'hooge, Jan ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Lab Cardiovasc Imaging & Dynam, Dept Cardiovasc Sci, Leuven, Belgium

[2] Univ Bologna, Dept Elect Elect & Informat Engn, Bologna, Italy

来源：

2016 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS) | 2016年

关键词：

SOCIETY;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Although a lot effort has been devoted over the past years to the accurate measurement of echocardiographic deformation curves in order to quantify regional myocardial function, much less attention has been paid to the problem of dealing with missing or artifactual curves. Considering the difficulties associated with missing or unreliable curves in the clinical diagnostic process, this study sought to examine the usefulness of the K-nearest neighbor (KNN) imputation algorithm to address this problem. Experiments with segmental strain (rate) curves of 30 normal subjects showed that the imputation algorithm can lead to low estimation errors even with a high percentage of missing data.

引用

页数：4

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