@article{DelVecchioHanafiPozzietal.2023,
  author    = {Del Vecchio, Jasmin and Hanafi, Ibrahem and Pozzi, Nicol{\´o} Gabriele and Capetian, Philipp and Isaias, Ioannis U. and Haufe, Stefan and Palmisano, Chiara},
  title     = {Pallidal recordings in chronically implanted dystonic patients: mitigation of tremor-related artifacts},
  series = {Bioengineering},
  volume    = {10},
  journal   = {Bioengineering},
  number    = {4},
  issn      = {2306-5354},
  doi       = {10.3390/bioengineering10040476},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313498},
  year      = {2023},
  abstract  = {Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the Percept\(^{TM}\) PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.},
  language  = {en}
}