title: Fusion of detected multi-channel maternal electrocardiogram (ECG) R-wave peak locations.
abstract: BACKGROUND
  Almost all promising non-invasive foetal ECG extraction methods involve accurately determining maternal ECG R-wave peaks. However, it is not easy to robustly detect accurate R-wave peaks of the maternal ECG component in an acquired abdominal ECG since it often has a low signal-to-noise ratio (SNR), sometimes containing a large foetal ECG component or other noises and interferences. This paper discusses, under the condition of acquiring multi-channel abdominal ECG signals, how to improve the robustness of maternal ECG R-wave peak detection.
  
  
  METHODS
  On the basis of summarising the current single channel ECG R-wave peak detection methods, the paper proposed a specific fusion algorithm of detected multi-channel maternal ECG R-wave peak locations. The proposed entire algorithm was then tested using two databases; one database, created by us, was composed of 343 groups of 8-channel data collected from 78 pregnant women, and the other one, called the challenge database, was from the Physionet/Computing in Cardiology Challenge 2013, including 175 groups of 4-channel data. When using these databases, each group of data was classified into two parts, called the training part and the validation test part respectively; the training part was the first 8.192 s of each group of data and the validation test part was the next 8.192 s.
  
  
  RESULTS
  To show the results, three evaluation parameters-sensitivity (Se), positive predictive value (PPV) and F1-are used. The validation test results for the database we collected are Se = 99.93 %, PPV = 99.98 %, and F1 = 99.95 %, while the results for the challenge database are Se = 99.91 %, PPV = 99.86 %, and F1 = 99.88 %.
  
  
  CONCLUSIONS
  The results of the test show that the robustness of our proposed whole fusion algorithm was superior to that of other outstanding algorithms for maternal R-wave detection, and is much better than that of single channel maternal R-wave detection algorithms.
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing, 210023, China. piaoliyin@163.com.
authors.forename: Qiong
authors.initials: Q
authors.lastname: Yu
authors.affiliation: Nanjing General Hospital of Nanjing Military Command, Nanjing, 210002, China. tsy8088@sina.com.
authors.forename: Qun
authors.initials: Q
authors.lastname: Guan
authors.affiliation: Nanjing General Hospital of Nanjing Military Command, Nanjing, 210002, China. zyyliping@sina.com.
authors.forename: Ping
authors.initials: P
authors.lastname: Li
authors.affiliation: Nanjing General Hospital of Nanjing Military Command, Nanjing, 210002, China. njbull@163.com.
authors.forename: Tie-Bing
authors.initials: TB
authors.lastname: Liu
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing, 210023, China. huang_xiaolin@163.com.
authors.forename: Xiao-Lin
authors.initials: XL
authors.lastname: Huang
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing, 210023, China. yingzhao@nju.edu.cn.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing, 210023, China. njhxliu@nju.edu.cn.
authors.forename: Hong-Xing
authors.initials: HX
authors.lastname: Liu
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing, 210023, China. yqwang@nju.edu.cn.
authors.forename: Yuan-Qing
authors.initials: YQ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D005323:Fetal Monitoring; D006801:Humans; D009035:Mothers; D011247:Pregnancy; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio