PMID- 24903422
OWN - NLM
STAT- MEDLINE
DCOM- 20140911
LR  - 20240321
IS  - 1475-925X (Electronic)
IS  - 1475-925X (Linking)
VI  - 13
DP  - 2014 Jun 5
TI  - Detection of inter-patient left and right bundle branch block heartbeats in ECG 
      using ensemble classifiers.
PG  - 72
LID - 10.1186/1475-925X-13-72 [doi]
AB  - BACKGROUND: Left bundle branch block (LBBB) and right bundle branch block (RBBB) 
      not only mask electrocardiogram (ECG) changes that reflect diseases but also 
      indicate important underlying pathology. The timely detection of LBBB and RBBB is 
      critical in the treatment of cardiac diseases. Inter-patient heartbeat 
      classification is based on independent training and testing sets to construct and 
      evaluate a heartbeat classification system. Therefore, a heartbeat classification 
      system with a high performance evaluation possesses a strong predictive 
      capability for unknown data. The aim of this study was to propose a method for 
      inter-patient classification of heartbeats to accurately detect LBBB and RBBB 
      from the normal beat (NORM). METHODS: This study proposed a heartbeat 
      classification method through a combination of three different types of 
      classifiers: a minimum distance classifier constructed between NORM and LBBB; a 
      weighted linear discriminant classifier between NORM and RBBB based on Bayesian 
      decision making using posterior probabilities; and a linear support vector 
      machine (SVM) between LBBB and RBBB. Each classifier was used with matching 
      features to obtain better classification performance. The final types of the test 
      heartbeats were determined using a majority voting strategy through the 
      combination of class labels from the three classifiers. The optimal parameters 
      for the classifiers were selected using cross-validation on the training set. The 
      effects of different lead configurations on the classification results were 
      assessed, and the performance of these three classifiers was compared for the 
      detection of each pair of heartbeat types. RESULTS: The study results showed that 
      a two-lead configuration exhibited better classification results compared with a 
      single-lead configuration. The construction of a classifier with good performance 
      between each pair of heartbeat types significantly improved the heartbeat 
      classification performance. The results showed a sensitivity of 91.4% and a 
      positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a 
      positive predictive value of 88.8% for RBBB. CONCLUSIONS: A multi-classifier 
      ensemble method was proposed based on inter-patient data and demonstrated a 
      satisfactory classification performance. This approach has the potential for 
      application in clinical practice to distinguish LBBB and RBBB from NORM of 
      unknown patients.
FAU - Huang, Huifang
AU  - Huang H
AD  - 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.
FAU - Liu, Jie
AU  - Liu J
FAU - Zhu, Qiang
AU  - Zhu Q
FAU - Wang, Ruiping
AU  - Wang R
FAU - Hu, Guangshu
AU  - Hu G
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20140605
PL  - England
TA  - Biomed Eng Online
JT  - Biomedical engineering online
JID - 101147518
SB  - IM
MH  - Bayes Theorem
MH  - Bundle-Branch Block/*physiopathology
MH  - Discriminant Analysis
MH  - Electrocardiography/*methods
MH  - Humans
MH  - *Signal Processing, Computer-Assisted
MH  - Statistics as Topic/*methods
MH  - Support Vector Machine
PMC - PMC4086987
EDAT- 2014/06/07 06:00
MHDA- 2014/09/12 06:00
PMCR- 2014/06/05
CRDT- 2014/06/07 06:00
PHST- 2014/02/05 00:00 [received]
PHST- 2014/05/19 00:00 [accepted]
PHST- 2014/06/07 06:00 [entrez]
PHST- 2014/06/07 06:00 [pubmed]
PHST- 2014/09/12 06:00 [medline]
PHST- 2014/06/05 00:00 [pmc-release]
AID - 1475-925X-13-72 [pii]
AID - 10.1186/1475-925X-13-72 [doi]
PST - epublish
SO  - Biomed Eng Online. 2014 Jun 5;13:72. doi: 10.1186/1475-925X-13-72.