Abstract
This work presents a noise cancellation system suitable for different biomedical signals based on a multilayer artifical neural network(ANN). The proposed method consists of a simple structure similar to the MADALINE neuronal network (Multiple ADAptive LINear Element). This network is a grown artificial neuronal network which allows to optimize the number of nodes of one hidden layer and coefficients of several matrixes. These coefficients matrixes are optimized using the Widrow-Hoff Delta algorithm which requires smaller computational cost than the required by the back-propagation algorithm.
The method’s performance has been obtained by computing the cross correlation between the input and the output signals to the system. In addition, the signal to interference ratio (SIR) has also been computed. Making use of the aforementioned indexes it has been possible to compare the different classical methods (Filter FIR, biorthogonal Wavelet 6,8, Filtered Adaptive LMS) and the proposed system based on neural multilayer networks . The comparison shows that the ANN-based method is able to better preserve the signal waveform at system output with an improved noise reduction in comparison with traditional techniques. Moreover, the ANN technique is able to reduce a great variety of noise signals present in biomedical recordings, like high frequency noise, white noise, movement artifacts and muscular noise.
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Mateo Sotos, J., Sánchez Meléndez, C., Vayá Salort, C., Cervigon Abad, R., Rieta Ibáñez, J.J. (2007). A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_38
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DOI: https://doi.org/10.1007/978-3-540-73053-8_38
Publisher Name: Springer, Berlin, Heidelberg
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