A multisensor data fusion approach for improving the classification accuracy of uterine EMG signals

@article{Moslem2011AMD,
  title={A multisensor data fusion approach for improving the classification accuracy of uterine EMG signals},
  author={Bassam Moslem and Mohamad Khalil and Mohamad O. Diab and Aly Chkeir and Catherine Marque},
  journal={2011 18th IEEE International Conference on Electronics, Circuits, and Systems},
  year={2011},
  pages={93-96}
}
Multisensor data fusion is an important technique used for solving various pattern recognition problems. In this paper, we used data fusion for improving the classification of uterine electromyogram (EMG) signals recorded by 16 electrodes positioned on the abdominal wall of the pregnant women. First, we evaluated the classification performance of each channel. Then, we applied a decision-level fusion method based first on the majority voting (MV), then on the weighted majority voting (WMV… CONTINUE READING