Soft Computing

, Volume 18, Issue 3, pp 571–578 | Cite as

An intelligent biological inspired evolutionary algorithm for the suppression of incubator interference in premature infants ECG

Methodologies and Application

Abstract

Electromagnetic interference produced by the incubator medical equipments may interrupt or degrade the premature infant’s electrocardiography (ECG) signal. The premature infant’s ECG is usually contaminated by an interference caused by the incubator devices. The interference cancellation system is designed using an adaptive learning ability of artificial neural network Levenberg–Marquardt (LM) algorithm. In this paper the swarm intelligent-LM algorithm is used for the electromagnetic interference cancellation in infant ECG signal. The swarm intelligent algorithm is used for the optimization by selecting the optimized number of neurons in the hidden layer, learning rate and momentum factor of the neural network. Also, this paper presents a comparison of residual mean square error (RMSE) values for neural network trained by LM algorithm, hybrid genetic-LM algorithm and hybrid swarm intelligent-LM algorithm. The LM algorithm is used for the weight updating and reducing the content of electromagnetic interference noise present in the signal. The performance analysis of the proposed noise cancellation approach is compared with gradient based and evolutionary based algorithms. The result analysis shows that the interferences in infant ECG signal is removed successfully using the proposed approach.

Keywords

Genetic algorithm Infant incubator Electromagnetic interferences Infant ECG signal Swarm intelligent Artificial neural network Levenberg–Marquardt algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringUdaya School of EngineeringVellamodiIndia
  2. 2.Department of Electrical and Electronics EngineeringAnna UniversityTiruchirapalliIndia

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