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Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems

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Listed:
  • Dongbao Jia
  • Cunhua Li
  • Qun Liu
  • Qin Yu
  • Xiangsheng Meng
  • Zhaoman Zhong
  • Xinxin Ban
  • Nizhuan Wang
  • Dan Selisteanu

Abstract

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.

Suggested Citation

  • Dongbao Jia & Cunhua Li & Qun Liu & Qin Yu & Xiangsheng Meng & Zhaoman Zhong & Xinxin Ban & Nizhuan Wang & Dan Selisteanu, 2021. "Application and Evolution for Neural Network and Signal Processing in Large-Scale Systems," Complexity, Hindawi, vol. 2021, pages 1-7, April.
    • Handle: RePEc:hin:complx:6618833
    DOI: 10.1155/2021/6618833
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