DocumentCode :
2468275
Title :
Adaptive threshold spike detection using stationary wavelet transform for neural recording implants
Author :
Yang, Yuning ; Kamboh, Awais ; Andrew, J Mason
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
fYear :
2010
fDate :
3-5 Nov. 2010
Firstpage :
9
Lastpage :
12
Abstract :
Spike detection is an essential first step in the analysis of neural recording signals. A new spike detection hardware architecture combining absolute threshold method and stationary wavelet transform (SWT) is described. The method enables spike detection with 90% accuracy even when the signal-to-noise is -1dB. A noise monitoring block was implemented to automatically calculate the appropriate threshold value for spike detection, and the system then chooses either absolute threshold method or the SWT method to optimize power consumption. The system was designed in 130nm CMOS and shown to occupy 0.082 mm2 and dissipate 0.45 μW for one channel.
Keywords :
CMOS integrated circuits; adaptive signal detection; medical signal detection; neurophysiology; prosthetics; wavelet transforms; 130nm CMOS; absolute threshold method; adaptive threshold spike detection; neural recording implants; neural recording signals; noise monitoring block; spike detection hardware architecture; stationary wavelet transform; Discrete wavelet transforms; Low pass filters; Monitoring; Noise measurement; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Circuits and Systems Conference (BioCAS), 2010 IEEE
Conference_Location :
Paphos
Print_ISBN :
978-1-4244-7269-7
Electronic_ISBN :
978-1-4244-7268-0
Type :
conf
DOI :
10.1109/BIOCAS.2010.5709558
Filename :
5709558
Link To Document :
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