• DocumentCode
    653443
  • Title

    Spike Detection Based on Fractal Dimension

  • Author

    Zhou Jiyang ; Xu Shengwei ; Lin Nansen ; Wang Mixia ; Cai Xinxia

  • Author_Institution
    State Key Lab. of Transducer Technol., Inst. of Electron., Beijing, China
  • fYear
    2013
  • fDate
    20-23 Aug. 2013
  • Firstpage
    1834
  • Lastpage
    1838
  • Abstract
    Spikes detection is very important to the neural information study. However, the original neural signals collected by the microelectrode contain a lot of noise. Sometimes, spikes detection is hard to achieve when SNR (Signal to Noise Rate) is very low. At present, the fractal theory has been widely applied, and fractal dimension is very sensitive to fluctuation of curves. The fractal theory is introduced to the preprocessing of neural signals in this paper. It detected spikes by calculating fractal dimension of the original data. Experiments show that, fractal dimension can sign fluctuation of curve. This method can effectively detect the low amplitude spikes in the noise. The effect of spike detection based on fractal dimension is better than the usual threshold method and energy method.
  • Keywords
    biomedical electrodes; fractals; medical signal detection; neurophysiology; SNR; amplitude spikes; curves fluctuation; energy method; fractal dimension; fractal theory; microelectrode; neural information; neural signals preprocessing; signal to noise rate; spikes detection; threshold method; Algorithm design and analysis; Equations; Fluctuations; Fractals; Mathematical model; Signal to noise ratio; fractal dimension; signal-to-noise ratio; spike;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/GreenCom-iThings-CPSCom.2013.340
  • Filename
    6682351