NDLI: A new adaptive approach to remove baseline wander from ECG recordings using Madeline structure

Content Provider	IEEE Xplore Digital Library
Author	Mateo, J. Sanchez, C. Vaya, C. Cervigon, R. Rieta, J.J.
Copyright Year	2007
Description	Author affiliation: Innovation in Bioeng. Res. Group, Univ. of Castilla-La Mancha, Cuenca (Mateo, J.; Sanchez, C.; Vaya, C.; Cervigon, R.) \|\| Biomed. Synergy, Valencia Univ. of Technol., Valencia (Rieta, J.J.)
Abstract	Nowadays, there exist different approaches to cancel out noise effect and baseline drift in biomedical signals. However, none of them can be considered as completely satisfactory. In this work, an artificial neural network (ANN) based approach to cancel out baseline drift in electrocardiogram signals is presented. The system is based on a grown ANN allowing to optimize both the hidden layer number of nodes and the coefficient matrixes. These matrixes are optimized following the Window-Hoff Delta algorithm, offering much lower computational cost that the traditional back propagation algorithm. The proposed methodology has been compared with traditional baseline reduction methods (FIR, Wavelet and Adaptive LMS filtering) making use of cross correlation, signal to interference ratio and signal to noise ratio indexes. Obtained results show that the ANN-based approach performs better, with respect to baseline drift reduction and signal distortion at filter output, than traditional methods.
Starting Page	533
Ending Page	536
File Size	1740182
Page Count	4
File Format	PDF
ISBN	9781424425334
ISSN	02766547
DOI	10.1109/CIC.2007.4745540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Computers in Cardiology(CinC)
Subject Keyword	Electrocardiography Artificial neural networks Finite impulse response filter Noise cancellation Computational efficiency Least squares approximation Adaptive filters Filtering Interference Signal to noise ratio
Content Type	Text
Resource Type	Article

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