DocumentCode :
678915
Title :
Developing non-parametric density estimation on genetic evolution computing as a cloud based sensor fusion method: Taking psychiatric major depressive disorder detection as an application example
Author :
Tsu-Wang Shen ; Fang-Chih Liu ; Chen, William Shao-Tsu
Author_Institution :
Dept. of Med. Inf., Tzu Chi Univ., Hualien, Taiwan
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
56
Lastpage :
61
Abstract :
Biomedical cloud computing offers on-demand healthcare services. A sensor fusion method is developed based on non-parametric density estimation on genetic evolution computing. Our method provides a potential solution for decision making on flicking features when not all measurements of sensors appear at the input end. The method was applied on major depressive disorder detection as an application example and it was successfully for MDD classification regardless different combinations of sensor monitoring.
Keywords :
cloud computing; genetic algorithms; health care; medical diagnostic computing; medical disorders; psychology; sensor fusion; biomedical cloud computing; cloud based sensor fusion method; genetic evolution computing; nonparametric density estimation; on-demand healthcare services; psychiatric major depressive disorder detection; Accuracy; Biomedical measurement; Cloud computing; Estimation; Genetic algorithms; Medical diagnostic imaging; Support vector machines; Cloud Computing; Depressive disorder; Genetic Evolution; Non-Parametric Density Estimation; Sensor Fusion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sensing Technology (ICST), 2013 Seventh International Conference on
Conference_Location :
Wellington
ISSN :
2156-8065
Print_ISBN :
978-1-4673-5220-8
Type :
conf
DOI :
10.1109/ICSensT.2013.6727616
Filename :
6727616
Link To Document :
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