A criterion for signal-based selection of wavelets for denoising intrafascicular nerve recordings

In this paper we propose a novel method for denoising intrafascicular nerve signals with the aim of improving action potential (AP) detection. The method is based on the stationary wavelet transform and thresholding of the wavelet coefficients. Since the choice of the mother wavelet substantially impact the performance, a criterion is proposed for selecting the optimal wavelet. The criterion for selection was based on the root mean square of the average of the output signal triggered by the detected APs. The mother wavelet was parameterized through the scaling filter, which allowed optimization through the proposed criterion. The method was tested on simulated signals and on experimental neural recordings. Experimental signals were recorded from the tibial branch of the sciatic nerve of three anaesthetized New Zealand white rabbits during controlled muscle stretches. The simulation results showed that the proposed method had an equivalent effect on AP detection performance (percentage of correct detection at 6 dB signal-to-noise ratio, mean ± SD, 95.3 ± 5.2%) to the a-posteriori choice of the best wavelet (96.1 ± 3.6). Moreover, the AP detection after the proposed denoising method resulted in a correlation of 0.94 ± 0.02 between the estimated spike rate and the muscle length. Therefore, the study proposes an effective method for selecting the optimal mother wavelet for denoising neural signals with the aim of improving AP detection.