Next Article in Journal
Railway Tunnel Clearance Inspection Method Based on 3D Point Cloud from Mobile Laser Scanning
Next Article in Special Issue
A Component-Based Approach for Securing Indoor Home Care Applications
Previous Article in Journal
Providing Personalized Energy Management and Awareness Services for Energy Efficiency in Smart Buildings
Previous Article in Special Issue
Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare
 
 
Article

A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres

1
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Department of Computer Engineering, Vienna University of Technology, Vienna 1040, Austria
3
State Key Laboratory of Applied Optics, Chinese Academy of Sciences, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 2049; https://doi.org/10.3390/s17092049
Received: 25 July 2017 / Revised: 4 September 2017 / Accepted: 5 September 2017 / Published: 7 September 2017
(This article belongs to the Special Issue Sensor-based E-Healthcare System: Greenness and Security)
Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates “hyperspheres” to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods. View Full-Text
Keywords: multi-sensor data fusion; support vector hypersphere; computer-aided diagnosis; trial hypertrophy multi-sensor data fusion; support vector hypersphere; computer-aided diagnosis; trial hypertrophy
Show Figures

Figure 1

MDPI and ACS Style

Zhu, Y.; Liu, D.; Grosu, R.; Wang, X.; Duan, H.; Wang, G. A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres. Sensors 2017, 17, 2049. https://doi.org/10.3390/s17092049

AMA Style

Zhu Y, Liu D, Grosu R, Wang X, Duan H, Wang G. A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres. Sensors. 2017; 17(9):2049. https://doi.org/10.3390/s17092049

Chicago/Turabian Style

Zhu, Yungang, Dayou Liu, Radu Grosu, Xinhua Wang, Hongying Duan, and Guodong Wang. 2017. "A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres" Sensors 17, no. 9: 2049. https://doi.org/10.3390/s17092049

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop