A Real-Time Artifact Removal System for Closed-Loop Deep-Brain Stimulation

This paper presents a novel real-time signal processing method for removing local field potential (LFP) artifacts during deep-brain stimulation (DBS). Real-time artifact removal is essential for closed-loop DBS systems, as they rely on real-time, artifact-free LFPs to provide stimulation feedback. Building on previous stimulation-sampling synchronization methods, this work introduces a dynamic template subtraction method that achieves precise and efficient real-time removal of stimulation artifacts. By leveraging stimulation-sampling synchronization, the method enables real-time template alignment and artifact removal through subtraction. It can operate even at low sampling rates, requiring a minimum of twice the stimulation frequency. The artifact templates are dynamically updated to adapt to changes in stimulation artifacts, ensuring robust and accurate performance over time. The method was evaluated through simulations and in vitro and in vivo experiments. Simulation tests validated its theoretical feasibility, while it successfully removed stimulation artifacts in vitro, relative errors in the power spectral density between the recovered and reference LFPs in the examined frequency band (1—150 Hz) were 0.64%, 0.31%, 0.58%, and 0.73% under stimulation at 20, 60, 90, and 130 Hz, respectively. In vivo, the method successfully recorded artifact-free LFPs in real time and supported beta-triggered closed-loop DBS. In an additional in vivo evaluation using a commercial medical device, the method recorded artifact-free LFPs with a sampling rate of 260 Hz (twice the stimulation frequency of 130 Hz). The proposed artifact removal method provides important technical support for realizing lightweight closed-loop DBS systems.