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首页> 外文会议>International Work-Conference on the Interplay Between Natural and Artificial Computation >A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals
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A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals

机译：基于学习的Widrow-Hoff Delta算法，用于生物医学信号的降噪

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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.
机译：该工作介绍了一种基于多层人工神经网络（ANN）的不同生物医学信号的噪声消除系统。该方法包括类似于Madaline神经元网络（多个自适应线性元件）的简单结构。该网络是一种生长的人工神经网络，其允许优化一个隐藏层的节点数量和几个矩阵的系数。这些系数矩阵使用Widrow-Hoff Delta算法进行了优化，该算法需要比背传播算法所需的计算成本更小。通过计算输入和输出信号与系统之间的互相关来获得该方法的性能。另外，还计算了对干扰比（SIR）的信号。利用上述索引可以比较不同的经典方法（过滤FIR，Biorthonal小波6,8，基于神经多层网络的所提出的系统。比较表明，基于ANN的方法能够更好地保留系统输出时的信号波形，与传统技术相比，降噪得到改善。此外，ANN技术能够减少生物医学记录中存在的各种噪声信号，如高频噪声，白噪声，运动伪像和肌肉噪声。

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