Objective. Implanted electrical stimulators with sensing capabilities have enabled the development of closed-loop neuromodulation therapies capable of responding to patient needs in real-time. Through a combination of rechargeable technologies and wireless data transmission, it is now possible for researchers to acquire extensive neural recordings from human participants in naturalistic settings using these bidirectional devices. However, data losses during wireless transmission hamper processing and the identification of neural signals of interest, driving the need for methodologies to properly estimate the impact of data loss. Approach. To accurately reconstruct the timing of data containing losses, we have developed a method called Periodic Estimation of Lost Packets (PELP) to precisely determine the number of samples lost from implanted recordings during active stimulation. PELP leverages a data-driven procedure for determining the period of stimulation and the knowledge that stimulation continues identically during periods where data are missing to accurately account for the number of samples lost. Main results. Using simulated stimulation added to collected human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Lastly, we successfully applied PELP to local field potential (LFP) recordings from an implanted, bidirectional device using data recorded in the clinic and the patient's own home. Significance. By effectively accounting for the timing of missing data, PELP enables the analysis of complex, naturalistic neural time series data from bidirectional implanted devices aiding in the development of novel therapeutic approaches. NCT04806516 (ClinicalTrials.gov).
Deep brain stimulation (DBS) therapies have shown clinical success in the treatment of a number of neurological illnesses, including obsessive-compulsive disorder, epilepsy, and Parkinson's disease. An emerging strategy for increasing the efficacy of DBS therapies is to develop closed-loop, adaptive DBS systems that can sense biomarkers associated with particular symptoms and in response, adjust DBS parameters in real-time. The development of such systems requires extensive analysis of the underlying neural signals while DBS is on, so that candidate biomarkers can be identified and the effects of varying the DBS parameters can be better understood. However, DBS creates high amplitude, high frequency stimulation artifacts that prevent the underlying neural signals and thus the biological mechanisms underlying DBS from being analyzed. Additionally, DBS devices often require low sampling rates, which alias the artifact frequency, and rely on wireless data transmission methods that can create signal recordings with missing data of unknown length. Thus, traditional artifact removal methods cannot be applied to this setting. We present a novel periodic artifact removal algorithm for DBS applications that can accurately remove stimulation artifacts in the presence of missing data and in some cases where the stimulation frequency exceeds the Nyquist frequency. The numerical examples suggest that, if implemented on dedicated hardware, this algorithm has the potential to be used in embedded closed-loop DBS therapies to remove DBS stimulation artifacts and hence, to aid in the discovery of candidate biomarkers in real-time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.