<b><i>Background:</i></b> The Medtronic “Percept” is the first FDA-approved deep brain stimulation (DBS) device with sensing capabilities during active stimulation. Its real-world signal-recording properties have yet to be fully described. <b><i>Objective:</i></b> This study details three sources of artifact (and potential mitigations) in local field potential (LFP) signals collected by the Percept and assesses the potential impact of artifact on the future development of adaptive DBS (aDBS) using this device. <b><i>Methods:</i></b> LFP signals were collected from 7 subjects in both experimental and clinical settings. The presence of artifacts and their effect on the spectral content of neural signals were evaluated in both the stimulation ON and OFF states using three distinct offline artifact removal techniques. <b><i>Results:</i></b> Template subtraction successfully removed multiple sources of artifact, including (1) electrocardiogram (ECG), (2) nonphysiologic polyphasic artifacts, and (3) ramping-related artifacts seen when changing stimulation amplitudes. ECG removal from stimulation ON (at 0 mA) signals resulted in spectral shapes similar to OFF stimulation spectra (averaged difference in normalized power in theta, alpha, and beta bands ≤3.5%). ECG removal using singular value decomposition was similarly successful, though required subjective researcher input. QRS interpolation produced similar recovery of beta-band signal but resulted in residual low-frequency artifact. <b><i>Conclusions:</i></b> Artifacts present when stimulation is enabled notably affected the spectral properties of sensed signals using the Percept. Multiple discrete artifacts could be successfully removed offline using an automated template subtraction method. The presence of unrejected artifact likely influences online power estimates, with the potential to affect aDBS algorithm performance.
BackgroundMany adaptative deep brain stimulation (DBS) paradigms rely upon the ability to sense neural signatures of specific clinical signs or symptoms in order to modulate therapeutic stimulation. In first-generation bidirectional neurostimulators, the ability to sense neural signals during active stimulation was often limited by artifact. Newer devices, with improved design specifications for sensing, have recently been developed and are now clinically available.ObjectiveTo compare the sensing capabilities of the first-generation Medtronic PC + S and second-generation Percept PC neurostimulators within a single patient.MethodsA 42-year-old man with Parkinson’s disease was initially implanted with left STN DBS leads connected to a PC + S implantable pulse generator. Four years later, the PC + S was replaced with the Percept PC. Local field potential (LFP) signals were recorded, both with stimulation OFF and ON, at multiple timepoints with each device and compared. Offline processing of time series data included artifact removal using digital filtering and template subtraction, before subsequent spectral analysis. With Percept PC, embedded processing of spectral power within a narrow frequency band was also utilized.ResultsIn the absence of stimulation, both devices demonstrated a peak in the beta range (approximately 20 Hz), which was stable throughout the 4-year period. Similar to previous reports, recordings with the PC + S during active stimulation demonstrated significant stimulation artifact, limiting the ability to recover meaningful LFP signal. In contrast, the Percept PC, using the same electrodes and stimulation settings, produced time series data during stimulation with spectral analysis revealing a peak in the beta-band. Online analysis by the Percept demonstrated a reduction in beta-band activity with increasing stimulation amplitude.ConclusionThis report highlights recent advances in implantable neurostimulator technology for DBS, demonstrating improvements in sensing capabilities during active stimulation between first- and second-generation devices. The ability to reliably sense during stimulation is an important step toward both the clinical implementation of adaptive algorithms and the further investigation into the neurophysiology underlying movement disorders.
1.AbstractDeep brain stimulation is a widely used therapy for Parkinson’s disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of “adaptive” neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.
This is a comprehensive, low-cost, and non-proprietary solution that provides unprecedented versatility of configuration to direct myoelectric prostheses without wired connections to the body. The amenability of MRC to varied coil geometries and arrangements has the potential to improve the efficiency and robustness of wireless power transfer links at all levels of upper-limb amputation. Additionally, the wireless recording device's programmable flash memory and selectable features will grant clinicians the unique ability to adapt and personalize the recording system's functional protocol for patient- or algorithm-specific needs.
Background: The Medtronic "Percept" is the first FDA approved deep brain stimulation (DBS) device with sensing capabilities during active stimulation. Its real-world signal recording properties have yet to be fully described. Objective: This study details sources of artifact (and potential mitigations) in local field potential (LFP) signals collected by the Percept, and assesses the potential impact of artifact on the future development of adaptive DBS (aDBS) using this device. Methods: LFP signals were collected from seven subjects in both experimental and clinical settings. The presence of artifacts and their effect on the spectral content of neural signals were evaluated in both the stimulation ON and OFF states using three distinct offline artifact removal techniques. Results: Template subtraction successfully removed multiple sources of artifact, including 1) electrocardiogram (ECG), 2) non-physiologic polyphasic artifacts, and 3) ramping related artifacts seen when changing stimulation amplitudes. ECG removal from stimulation ON (at 0 mA) signals recovered the spectral shape seen when OFF stimulation (averaged difference in normalized power in theta, alpha, and beta bands ≤ 3.5%). ECG removal using singular value decomposition was similarly successful, though required subjective researcher input. QRS interpolation produced similar recovery of beta-band signal, but resulted in residual low-frequency artifact. Conclusions: Artifacts present when stimulation is enabled notably affected the spectral properties of sensed signals using the Percept. Multiple discrete artifacts could be successfully removed offline using an automated template subtraction method. The presence of unrejected artifact likely influences online power estimates, with the potential to affect aDBS algorithm performance.
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