Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Department of Computer Engineering, Vienna University of Technology, Vienna 1040, Austria.
Sensors (Basel). 2017 Sep 7;17(9):2049. doi: 10.3390/s17092049.
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.
基于从患者获取的多个医学传感器数据进行疾病诊断是基于传感器的电子医疗保健系统中的关键任务。然而，基于传感器数据融合的心房肥大疾病的有效诊断方法很少，这是一个具有挑战性的问题。在本文中，我们提出了一种用于心房肥大诊断的新型多传感器数据融合方法，即特征化支持向量超球体（CSVH）。与传统的支持向量机不同，该方法不是构建一个超平面，而是生成“超球体”来收集有区别的医学信息，因为超球体比超平面更强大，更适合数据描述。具体来说，CSVH 分别为患者和健康受试者的类别构建两个特征化的超球体。患者类别的超球体是在加权版本中构建的，以考虑患者实例的多样性。健康人群类别的超球体与患者类别的距离最远，以实现与患者类别的最大分离。查询通过基于两个超球体定义的隶属函数进行标记。如果查询被两个类别拒绝，则会调查查询到异常值和重叠区域数据的角度信息，以提供最终决策。实验结果表明，该方法在最先进的方法中实现了最高的诊断准确性。
