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

共 50 条

[41] A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering
Vajda, Szilard
Santosh, K. C.
RECENT TRENDS IN IMAGE PROCESSING AND PATTERN RECOGNITION (RTIP2R 2016), 2017, 709 : 185 - 193
[42] Approximate direct and reverse nearest neighbor queries, and the k-nearest neighbor graph
Figueroa, Karina
Paredes, Rodrigo
SISAP 2009: 2009 SECOND INTERNATIONAL WORKSHOP ON SIMILARITY SEARCH AND APPLICATIONS, PROCEEDINGS, 2009, : 91 - +
[43] Predicting Math Test Scores Using K-Nearest Neighbor
Brown, Jessica Maikhanh
PROCEEDINGS OF THE 2017 7TH IEEE INTEGRATED STEM EDUCATION CONFERENCE (ISEC), 2017, : 104 - 106
[44] Fast agglomerative clustering using a k-nearest neighbor graph
Franti, Pasi
Virmajoki, Olli
Hautamaki, Ville
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006, 28 (11) : 1875 - 1881
[45] Parallel Computation of k-Nearest Neighbor Joins Using MapReduce
Kim, Wooyeol
Kim, Younghoon
Shim, Kyuseok
2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 696 - 705
[46] Manifold learning using Euclidean k-nearest neighbor graphs
Costa, JA
Hero, AO
2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PROCEEDINGS: IMAGE AND MULTIDIMENSIONAL SIGNAL PROCESSING SPECIAL SESSIONS, 2004, : 988 - 991
[47] Fault Identification Using Fast k-Nearest Neighbor Reconstruction
Zhou, Zhe
Li, Zuxin
Cai, Zhiduan
Wang, Peiliang
PROCESSES, 2019, 7 (06)
[48] Microarray Data Classification using Fuzzy K-Nearest Neighbor
Kumar, Mukesh
Rath, Santanu Ku
2014 INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), 2014, : 1032 - 1038
[49] Using k-Nearest Neighbor and Speaker Ranking for Phoneme Prediction
Rizwan, Muhammad
Anderson, David V.
2014 13TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2014, : 383 - 387
[50] A fall detection system using k-nearest neighbor classifier
Liu, Chien-Liang
Lee, Chia-Hoang
Lin, Ping-Min
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (10) : 7174 - 7181

← 1 2 3 4 5 →