Implantable neural spike detection using lifting-based stationary wavelet transform

Spike detection from high data rate neural recordings is desired to ease the bandwidth bottleneck of bio-telemetry. An appropriate spike detection method should be able to detect spikes under low signal-to-noise ratio (SNR) while meeting the power and area constraints of implantation. This paper introduces a spike detection system utilizing lifting-based stationary wavelet transform (SWT) that decomposes neural signals into 2 levels using ‘symmlet2’ wavelet basis. This approach enables accurate spike detection down to an SNR of only 2. The lifting-based SWT architecture permits a hardware implementation consuming only 6.6 μW power and 0.07mm2 area for 32 channels with 3.2 MHz master clock.

[1]  Qi Zhao,et al.  Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording , 2009, NIPS.

[2]  Sung June Kim,et al.  A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio , 2003, IEEE Transactions on Biomedical Engineering.

[3]  Joel W. Burdick,et al.  Spike detection using the continuous wavelet transform , 2005, IEEE Transactions on Biomedical Engineering.

[4]  Patrick D. Wolf,et al.  Evaluation of spike-detection algorithms fora brain-machine interface application , 2004, IEEE Transactions on Biomedical Engineering.

[5]  M. Sawan,et al.  An Ultra Low-Power CMOS Automatic Action Potential Detector , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Yuning Yang,et al.  Adaptive threshold spike detection using stationary wavelet transform for neural recording implants , 2010, 2010 Biomedical Circuits and Systems Conference (BioCAS).

[7]  Chang-Soo Lee,et al.  New lifting based structure for undecimated wavelet transform , 2000 .

[8]  Awais M. Kamboh,et al.  Area-Power Efficient VLSI Implementation of Multichannel DWT for Data Compression in Implantable Neuroprosthetics , 2007, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Reid R. Harrison,et al.  A low-power integrated circuit for adaptive detection of action potentials in noisy signals , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[10]  M. Aghagolzadeh,et al.  Compressed and Distributed Sensing of Neuronal Activity for Real Time Spike Train Decoding , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  David C. Martin,et al.  Chronic neural recordings using silicon microelectrode arrays electrochemically deposited with a poly(3,4-ethylenedioxythiophene) (PEDOT) film , 2006, Journal of neural engineering.

[12]  K. Wise,et al.  A three-dimensional microelectrode array for chronic neural recording , 1994, IEEE Transactions on Biomedical Engineering.