Electrocardiography Classification Based on Revised Locally Linear Embedding Algorithm and Kernel-Based Fuzzy C-Means Clustering
Electrocardiography Classification Based on Revised Locally Linear Embedding Algorithm and Kernel-Based Fuzzy C-Means Clustering
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DOI：
10.1166/jmihi.2014.1342
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发表时间：
2014-12
影响因子：
--
通讯作者：
Zhang Cong;Z. Shan;Zhang Hui
中科院分区：
文献类型：
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作者：
Zhang Cong;Z. Shan;Zhang Hui
提出了一种心电信号的分类方法。该方法首先使用一种改进的局部线性嵌入(LLE)算法对心电数据进行降维。这种基于流形距离测量的LLE是为了解决传统LLE由于使用欧氏距离而不能正确测量高维样本之间的距离的缺陷而提出的。使用这种改进的LLE算法对数据进行降维，可以保留更多的原始数据信息，更有效地提取高维心电数据的特征，从而提高分类精度。然后采用基于核的模糊C均值聚类算法对心电信号进行分类。对MIT-BIH数据库中四种常见类型的心电信号的分类测试表明，该方法的总体准确率达到99%。
This paper presents an electrocardiographic signal classification method. This method first uses a revised locally linear embedding (LLE) algorithm to perform dimension reduction to the Electrocardiography (ECG) data. This manifold distance measurement-based LLE was proposed to solve the defect of conventional LLE that, due to the use of Euclidean distance, it could not properly measure the distance between high-dimensional samples. Using this revised LLE algorithm for the dimension reduction of data, more original data information could be retained, and the features of high-dimensional ECG data could be more effectively extracted, thereby improving the classification accuracy. The method then adopts kernel-based fuzzy C-means clustering algorithm to perform ECG signal classification. Classification tests on four common types of ECG signals from the MIT-BIH database showed that the proposed method reached an overall accuracy of 99%.