Nugent C D, Webb J A, Black N D
The Northern Ireland BioEngineering Centre, Newtownabbey.
Med Inform Internet Med. 2000 Jul-Sep;25(3):225-35. doi: 10.1080/146392300750019217.
Two methodologies, feature and classifier fusion, for the problem of computerized 12-lead electrocardiogram classification, are investigated. Firstly, the entire classification problem is subdivided into a number of smaller bi-dimensional ones. By employing bi-group Neural Network classifiers, independent feature vectors for each diagnostic class are examined individually and the output from each classifier are fused together to produce one single result. Secondly, two classifiers, namely the aforementioned and a decision tree, are fused together through a novel approach of a Specificity Matrix. This methodology addresses the problem of unresolved conflict during fusion of classifiers and aims to exploit the merits of each classifier and suppress their weaknesses. 290 validated 12-lead electrocardiogram recordings, comprising six diagnostic classes, were used to train, validate and test both methodologies. The framework of bi-group classifiers enhanced the overall performance by 12.0% in comparison with conventional approaches. In the second instance, the fusion of the two classifiers produced a performance level of 81.3%; superior to either classifier in isolation. This approach offers a viable solution to the unresolved problem of conflict between classifiers during fusion and can be extended readily to accommodate any number of diagnostic classes and classifiers.
针对计算机化12导联心电图分类问题，研究了两种方法，即特征融合和分类器融合。首先，将整个分类问题细分为若干个较小的二维问题。通过使用双组神经网络分类器，对每个诊断类别的独立特征向量进行单独检查，并将每个分类器的输出融合在一起以产生一个单一结果。其次，通过一种新颖的特异性矩阵方法，将上述两种分类器（即双组神经网络分类器和决策树）融合在一起。该方法解决了分类器融合过程中未解决的冲突问题，旨在利用每个分类器的优点并抑制其缺点。使用包含六个诊断类别的290份经过验证的12导联心电图记录对这两种方法进行训练、验证和测试。与传统方法相比，双组分类器框架将整体性能提高了12.0%。在第二种情况下，两种分类器的融合产生了81.3%的性能水平；优于任何一个单独的分类器。该方法为分类器融合过程中未解决的冲突问题提供了一个可行的解决方案，并且可以很容易地扩展以适应任意数量的诊断类别和分类器。
