监督MCA降维框架中的心跳分类模型

Heart beat classification model with dimensionality reduction framework based on supervised MCA

高维心电图数据存在大量不相关特征,基于监督机器学习技术很难同时获得较高敏感性与特异性。在预处理操作心电图数据,如校准基线漂移、去除高频噪声和拟合多项式特征的基础上,提出一种基于监督多元对应分析(MCA)降维技术的分类模型自动分类心跳。该方法离散化连续心电图数据为类属数据,并发展有监督MCA降维技术提取心电图数据关键特征,用各种分类算法自动分类心电图心跳数据。在PTB诊断数据库的心电图数据集上测试结果表明,与几种基于监督机器学习分类技术相比,在监督MCA降维框架中各种分类算法能以较高敏感性和特异性自动分类心电图心跳数据。

There exist plenty uncorrelated features in the high dimensional ECG data, so, it is difficult for the classifier based on supervised learning to perform well in both sensitivity and specificity. Pre-processed by baseline wander removing, high-frequency span removing and polynomial fitting, an auto heartbeat classification model is proposed based on supervised MCA dimension reducing. The sequence ECG data is discretized; supervised MCA dimension reducing technology is employed to extract the key features; the ECG data is classified with the common classifiers. The experiment on the PTB database shows, compared with supervised learning method, this approach combining with different classifiers has a better performance on both sensitivity and specificity.