Wieben O, Afonso V X, Tompkins W J
Department of Electrical & Computer Engineering, University of Wisconsin, Madison, USA.
Med Biol Eng Comput. 1999 Sep;37(5):560-5. doi: 10.1007/BF02513349.
The classification of heart beats is important for automated arrhythmia monitoring devices. The study describes two different classifiers for the identification of premature ventricular complexes (PVCs) in surface ECGs. A decision-tree algorithm based on inductive learning from a training set and a fuzzy rule-based classifier are explained in detail. Traditional features for the classification task are extracted by analysing the heart rate and morphology of the heart beats from a single lead. In addition, a novel set of features based on the use of a filter bank is presented. Filter banks allow for time-frequency-dependent signal processing with low computational effort. The performance of the classifiers is evaluated on the MIT-BIH database following the AAMI recommendations. The decision-tree algorithm has a gross sensitivity of 85.3% and a positive predictivity of 85.2%, whereas the gross sensitivity of the fuzzy rule-based system is 81.3%, and the positive predictivity is 80.6%.
心跳分类对于自动心律失常监测设备很重要。该研究描述了两种用于识别体表心电图中心室早搏（PVC）的不同分类器。详细解释了基于从训练集进行归纳学习的决策树算法和基于模糊规则的分类器。通过分析单导联心跳的心率和形态来提取用于分类任务的传统特征。此外，还提出了一组基于滤波器组使用的新颖特征。滤波器组允许以低计算量进行时频相关的信号处理。按照美国医学仪器促进协会（AAMI）的建议，在麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）数据库上评估分类器的性能。决策树算法的总灵敏度为85.3%，阳性预测值为85.2%，而基于模糊规则系统的总灵敏度为81.3%，阳性预测值为80.6%。
