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
Link To Document :
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