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.