Objective: To study extracellular multi-neuron activity in the nervous system based on wavelet-fractal technique. Methods: The wavelet transform was employed to decompose the original signal and obtain 4 sub-patterns....Objective: To study extracellular multi-neuron activity in the nervous system based on wavelet-fractal technique. Methods: The wavelet transform was employed to decompose the original signal and obtain 4 sub-patterns. The dividing fractal dimensions of these sub-patterns were computed. A knn-classier was used to classify feature vectors. Results: Not all the elements in feature vector DimDC were very powerful for this pattern recognition problem through the empirical study of noise signals. The most effective feature vector was defined as DimDC= (d3:d4) above. Conclusion:Wavelet fractal algorithm has high accuracy and provides a powerful tool for clinical application.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60371034)
文摘Objective: To study extracellular multi-neuron activity in the nervous system based on wavelet-fractal technique. Methods: The wavelet transform was employed to decompose the original signal and obtain 4 sub-patterns. The dividing fractal dimensions of these sub-patterns were computed. A knn-classier was used to classify feature vectors. Results: Not all the elements in feature vector DimDC were very powerful for this pattern recognition problem through the empirical study of noise signals. The most effective feature vector was defined as DimDC= (d3:d4) above. Conclusion:Wavelet fractal algorithm has high accuracy and provides a powerful tool for clinical application.
