Abstract

The premature infant Electrocardiography (ECG) is always contaminated by an electromagnetic 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 a 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-Back Propagation Neural Network (PSO-BPNN) feed forward algorithm is used for the Active Noise Control (ANC) of the fundamental electromagnetic interference in the incubators. The results showed the incubator noise can be significantly reduced using the developed hybrid PSO-BPNN algorithm. To implement this process of noise cancellation, the software used is MATLAB 7.10 with the help of neural network toolbox.

Keywords:

Active noise control, back propagation algorithm, ECG signal, electromagnetic interferences, neural network, PSO,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.