Abstract
We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.
Highlights
Analyzing and understanding signals in human cerebral cortex, is the ultimate goal of many scientists in the field of neuroscience
In order to set a level for action potentials (APs) detection, we have simulated the response to threshold detection when changing the decomposition level after which we compute the threshold
Results show that for stationary wavelet transform (SWT) with a neural signal sampled at 10 kHz, the percentage of correct detection is the best when applying the threshold on the third level of wavelet decomposition
Summary
Analyzing and understanding signals in human cerebral cortex, is the ultimate goal of many scientists in the field of neuroscience. Decoding communication between neurons and networks is for example mandatory in prosthesis intended to overcome handicaps such as blindness or limb amputation (Burrow et al, 1997; Buffoni et al, 2003; Carmena et al, 2003; Rothschild, 2010). One bottleneck in this domain is the lack of generic rules for deciding neural signal. The spiking rate usually varies from 10 to 120 occurrences per second (Harrison et al, 2007). Automatic and real-time AP detection is essential for any application that requires continuous communication between excitable cells and electronics
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