title: Implantable neural spike detection using lifting-based stationary wavelet transform.
abstract: 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 0.07 mm(2) area for 32 channels with 3.2 MHz master clock.
authors.forename: Yuning
authors.initials: Y
authors.lastname: Yang
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Mason
mesh_terms: D000465:Algorithms; D003201:Computers; D004867:Equipment Design; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013686:Telemetry / Q000379:methods; D013997:Time Factors; D058067:Wavelet Analysis; D059015:Wireless Technology