Automatic classification of heartbeats using ECG morphology and heartbeat interval features
Automatic classification of heartbeats using ECG morphology and heartbeat interval features
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DOI：
10.1109/tbme.2004.827359
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发表时间：
2004-07-01
影响因子：
4.6
通讯作者：
Reilly, RB
中科院分区：
文献类型：
--
作者：
de Chazal, P;O'Dwyer, M;Reilly, RB
提出了一种用于心电分类的自动处理方法。该方法将人工检测到的心跳分配到ANSI/AAMI EC57:1998标准推荐的五种心跳类别之一，即正常心跳、心室异位心跳（VEB）、室上异位心跳（SVEB）、正常和VEB融合或未知心跳类型。数据来自MIT-BIH心律失常数据库的44个非起搏器记录。数据被分成两个数据集，每个数据集包含来自22个录音的大约50000次节拍。第一个数据集用于从候选配置中选择分类器配置。比较了由两个心电导联得到的12种配置处理特征集。特征集基于心电图形态、心跳间隔和rr间隔。所有配置都采用了利用监督学习的统计分类器模型。第二个数据集用于提供所选配置的独立性能评估。该评估的敏感性为75.9%，阳性预测率为38.5%，SVEB类的假阳性率为4.7%。VEB类的敏感性为77.7%，阳性预测率为81.9%，假阳性率为1.2%。这些结果是对先前报道的自动心跳分类系统结果的改进。
A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.