@article{MAKHILLIJSC20138321141,
    title = {Suppression of Electromagnetic Interference in ECG
Signal Using Artificial Intelligent Algorithm},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {3},
    pages = {192-198},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.192.198},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.192.198},
    author = {J. and},
    keywords = {Infant incubator,electromagnetic interferences,ECG signal,PSO,ANN,back propagation algorithm,active noise control},
    abstract = {Electromagnetic interference produced by the incubator medical 
  equipments may interrupt or degrade the premature infant Electrocardiography 
  (ECG) signal. The premature infant ECG is always contaminated by an interference 
  caused by the incubator devices. This study describes the interference noise 
  cancelling techniques for filtering of the corrupted infant ECG signal using 
  the biological inspired Particle Swarm Optimization (PSO) algorithm. The Active 
  Noise Control System is designed using an adaptive learning ability of artificial 
  neural network back propagation algorithm. The neural weights are adapted based 
  in PSO in an adaptive manner. In this study, the hybrid Particle Swarm Optimization-Artificial 
  Neural Network (PSO-ANN) feed forward algorithm is used for the Active Noise 
  Control (ANC) of the fundamental electromagnetic interference in the incubators. 
  The performance of the proposed noise cancellation approach is compared with 
  gradient based algorithms and this proposed approach is successfully removing 
  the noise.}
    }