Methods and system for recording human physiological signals from implantable leads during spinal cord stimulation.

This article presents a method-including hardware configuration, sampling rate, filtering settings, and other data analysis techniques-to measure evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) in humans with externalized percutaneous electrodes. The goal is to provide a robust and standardized protocol for measuring ECAPs on the non-stimulation contacts and to demonstrate how measured signals depend on hardware and processing decisions. Two participants were implanted with percutaneous leads for the treatment of chronic pain with externalized leads during a trial period for stimulation and recording. The leads were connected to a Neuralynx ATLAS system allowing us to simultaneously stimulate and record through selected electrodes. We examined different hardware settings, such as online filters and sampling rate, as well as processing techniques, such as stimulation artifact removal and offline filters, and measured the effects on the ECAPs metrics: the first negative peak (N1) time and peak-valley amplitude. For accurate measurements of ECAPs, the hardware sampling rate should be least at 8 kHz and should use a high pass filter with a low cutoff frequency, such as 0.1 Hz, to eliminate baseline drift and saturation (railing). Stimulation artifact removal can use a double exponential or a second-order polynomial. The polynomial fit is 6.4 times faster on average in computation time than the double exponential, while the resulting ECAPs' N1 time and peak-valley amplitude are similar between the two. If the baseline raw measurement drifts with stimulation, a median filter with a 100-ms window or a high pass filter with an 80-Hz cutoff frequency preserves the ECAPs. This work is the first comprehensive analysis of hardware and processing variations on the observed ECAPs from SCS leads. It sets recommendations to properly record and process ECAPs from the non-stimulation contacts on the implantable leads.

Informations de copyright

© 2023 Ramadan, König, Zhang, Ross, Herman, Netoff and Darrow.

Déclaration de conflit d'intérêts

TN and DD hold equity in and serve as officers for Stim Sherpa, which has licensed optimization IP from the University of Minnesota. This study was funded by Abbott Neuromodulation. The funder had the following involvement in the study: authors MZ and ER are employed by Abbott Neuromodulation, and contributed to data analysis and writing (MZ) as well as contributing to the design of the experiment and the manuscript review (MZ and ER).