Heartbeat Classification Using Normalized RR Intervals and Morphological Features
Heartbeat Classification Using Normalized RR Intervals and Morphological Features
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DOI：
10.1155/2014/712474
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发表时间：
2014-01-01
影响因子：
--
通讯作者：
Yang, Chun-Min
中科院分区：
文献类型：
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作者：
Lin, Chun-Cheng;Yang, Chun-Min
本研究开发了一个自动心跳分类系统，用于识别正常搏动，室上性异位搏动，室性异位搏动的基础上归一化的RR间期和形态特征。心跳分类系统由信号预处理、特征提取和线性判别分类组成。首先，信号预处理去除了原始心电信号中的高频噪声和基线漂移。特征提取采用小波分析和线性预测建模方法，得到归一化RR间期和两类形态特征。最后，线性判别分类器结合提取的特征分类心跳。从MIT-BIH心律失常数据库中获得的总共99，827次心跳被分为三个数据集，用于训练和测试优化的心跳分类系统。研究结果表明，使用归一化的RR间期特征大大提高了识别正常心跳的阳性预测准确性和识别室上性异位心跳的灵敏度相比，使用非归一化的RR间期特征。此外，小波和线性预测形态特征的组合比仅使用小波特征或线性预测特征具有更高的全局性能。
This study developed an automatic heartbeat classification system for identifying normal beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR intervals and morphological features. The proposed heartbeat classification system consists of signal preprocessing, feature extraction, and linear discriminant classification. First, the signal preprocessing removed the high-frequency noise and baseline drift of the original ECG signal. Then the feature extraction derived the normalized RR intervals and two types of morphological features using wavelet analysis and linear prediction modeling. Finally, the linear discriminant classifier combined the extracted features to classify heartbeats. A total of 99,827 heartbeats obtained from the MIT-BIH Arrhythmia Database were divided into three datasets for the training and testing of the optimized heartbeat classification system. The study results demonstrate that the use of the normalized RR interval features greatly improves the positive predictive accuracy of identifying the normal heartbeats and the sensitivity for identifying the supraventricular ectopic heartbeats in comparison with the use of the nonnormalized RR interval features. In addition, the combination of the wavelet and linear prediction morphological features has higher global performance than only using the wavelet features or the linear prediction features.