Department of Electrical and Computer Engineering, Carnegie Mellon University, PA 15213, USA.
IEEE Trans Biomed Eng. 2012 Oct;59(10):2930-41. doi: 10.1109/TBME.2012.2213253. Epub 2012 Aug 15.
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.
在本文中，我们提出了一种基于形态和动态特征相结合的新的心跳分类方法。小波变换和独立分量分析（ICA）分别应用于每个心跳以提取形态特征。此外，计算 RR 间隔信息以提供动态特征。这两种不同类型的特征被串联在一起，并使用支持向量机分类器将心跳分类为 16 个类别之一。该过程独立应用于来自两个 ECG 导联的数据，并且两个决策被融合以进行最终的分类决策。所提出的方法在基线 MIT-BIH 心律失常数据库上进行了验证，在“面向类”评估中总体准确率（即正确分类的心跳百分比）为 99.3%（99.7%，拒绝率为 2.4%），在“面向对象”评估中的准确率为 86.4%，与自动心跳分类的最新结果相当。
