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Sensors 2017, 17(9), 2049; doi:10.3390/s17092049

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.
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)
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Abstract

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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