A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals.
A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals.
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DOI：
10.1186/1475-925x-13-90
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发表时间：
2014-06-30
影响因子：
3.9
通讯作者：
Hu G
中科院分区：
文献类型：
--
作者：
Huang H;Liu J;Zhu Q;Wang R;Hu G
患者间分类模式和美国医疗器械进步协会（AAMI）标准对自动心跳分类系统的构建和评估具有重要意义。之前提出的方法，考虑到以上两个方面，大多数使用相同的特征和分类方法来对不同类型的心跳进行分类。分类系统在室性异搏（VEB）和室上异搏（SVEB）方面的表现往往不令人满意。基于VEB和SVEB的不同特点，构建了一种新的分层心跳分类系统。这样做是为了通过使用不同的特征和分类方法来提高这两类心跳的分类性能。首先，采用随机投影和支持向量机（SVM）集成方法检测VEB；然后，将RR区间的比值与预先确定的阈值进行比较，以检测SVEB。在训练集上选择分类模型的最优参数，并将其用于独立测试集，以评估分类系统的最终性能。同时，评价了不同导联构型对分类结果的影响。结果表明，该分类系统的分类性能明显优于其他分类方法。VEB的检测灵敏度为93.9%，阳性预测值为90.9%；SVEB的检测灵敏度为91.1%，阳性预测值为42.2%。此外，这种分类过程相对较快。提出了一种基于患者间数据划分的分层心跳分类系统，用于检测VEB和SVEB。与现有方法相比，该方法具有更好的分类性能。在临床实践中，它可以被认为是一种很有前途的检测未知患者VEB和SVEB的系统。
The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice.