Fusing evidences from intracranial pressure data using Dempster-Shafer theory

被引：0

作者：

Conte, R. ^{[1
]}

Longo, M. ^{[1
]}

Allai-Ano, S. ^{[1
]}

Matta, V. ^{[1
]}

Velardi, E. ^{[2
]}

机构：

[1] Univ Salerno, Dipartimento Ingn Informaz & Ingn Elettr DIIIE, Via Ponte Melillo, Fisciano, SA, Italy

[2] Ospedale Bambino Gesu, Unita Operativa Neurotraumatologia, DEA, Rome, Italy

来源：

PROCEEDINGS OF THE 2007 15TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING | 2007年

关键词：

intracranial pressure (ICP); Dempster-Shafer theory (DST). decision support; data fusion;

D O I：

10.1109/ICDSP.2007.4288543

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Intracranial pressure (ICP) data are used to extract some relevant features (mean value, average trend, waveform shape) from each of which a degree of confidence about the pathological, Lis opposed to physiological, state of the patient is assigned, These individual confidences are defined and successively fused fit a single global index, by exploiting Dempster-Shafer theory (DST). The approach represents a quite flexible and general framework that can be useful for supporting human decisions and, as such, has many potential medical applications.

引用

页码：159 / +

页数：2

共 50 条

[1] Dempster-Shafer Theory for Fusing Face Morphing Detectors
Makrushin, Andrey
Kraetzer, Christian
Dittmann, Jana
Seibold, Clemens
Hilsmann, Anna
Eisert, Peter
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2019,
[2] Integrated Data Fusion Using Dempster-Shafer Theory
Zhang, Yang
Zeng, Qing-An
Liu, Yun
Shen, Bo
2015 FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE THEORY, SYSTEMS AND APPLICATIONS (CCITSA 2015), 2015, : 98 - 103
[3] Shape from silhouette using Dempster-Shafer theory
Diaz-Mas, L.
Munoz-Salinas, R.
Madrid-Cuevas, F. J.
Medina-Carnicer, R.
PATTERN RECOGNITION, 2010, 43 (06) : 2119 - 2131
[4] Combination in Dempster-Shafer Theory Based on a Disagreement Factor Between Evidences
Abellan, Joaquin
Moral-Garcia, Serafin
Dolores Benitez, Maria
SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2019, 2019, 11726 : 148 - 159
[5] A generalization of entropy using Dempster-Shafer theory
Herencia, JA
Lamata, MT
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 2000, 29 (05) : 719 - 735
[6] Searching Attentive Tasks with Document Analysis Evidences and Dempster-Shafer Theory
Stamm, Kristin
Dengel, Andreas
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 529 - 532
[7] AN EXERCISE IN DEMPSTER-SHAFER THEORY
HAJEK, P
HARMANEC, D
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 1992, 20 (02) : 137 - 142
[8] A clash in Dempster-Shafer theory
Xiong, W
Ju, S
Luo, X
10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE, 2001, : 793 - 796
[9] Geospatial Modeling Using Dempster-Shafer Theory
Elmore, Paul A.
Petry, Frederick E.
Yager, Ronald R.
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (06) : 1551 - 1561
[10] Sensor fusion using Dempster-Shafer theory
Wu, HD
Siegel, M
Stiefelhagen, R
Yang, J
IMTC 2002: PROCEEDINGS OF THE 19TH IEEE INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, VOLS 1 & 2, 2002, : 7 - 12

← 1 2 3 4 5 →