@article{HaufeIsaiasPellegrinietal.2023,
  author    = {Haufe, Stefan and Isaias, Ioannis U. and Pellegrini, Franziska and Palmisano, Chiara},
  title     = {Gait event prediction using surface electromyography in parkinsonian patients},
  series = {Bioengineering},
  volume    = {10},
  journal   = {Bioengineering},
  number    = {2},
  issn      = {2306-5354},
  doi       = {10.3390/bioengineering10020212},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304380},
  year      = {2023},
  abstract  = {Gait disturbances are common manifestations of Parkinson's disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2\%), and next to no false-positive event detections (<0.1\%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.},
  language  = {en}
}
@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}
}