title: A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals.
abstract: BACKGROUND
  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).
  
  
  METHODS
  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
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, 3 Shang Yuan Cun, Hai Dian District, Beijing, China. hfhuang@bjtu.edu.cn.
authors: Huifang
authors: H
authors: Huang
authors: Jie
authors: J
authors: Liu
authors: Qiang
authors: Q
authors: Zhu
authors: Ruiping
authors: R
authors: Wang
authors: Guangshu
authors: G
authors: Hu
mesh_terms: D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic / Q000379:methods*; D060388:Support Vector Machine; D018879:Ventricular Premature Complexes / Q000503:physiopathology