Akademik Veri Yönetim Sistemi
Neurological Sciences and Neurophysiology, cilt.39, sa.4, ss.1-6, 2022 (SCI-Expanded)
Freezing of gait (FOG) is an important concern for both patients with
Parkinson’s disease (pwPD) and physicians. In this study, we aimed to introduce
a study protocol and our initial data. The data were subsequently used in machine
learning models to detect FOG episodes using brain activity signals and motion
data in the laboratory setting using complex FOG‑evoking activities in a sample
of pwPD with and without FOG compared with age‑matched healthy controls.
Subjects and Methods: An experimental task to evoke a FOG episode was
designed. This experimental task was tested on two pwPD with FOG in “on”
and “off” periods and one healthy control. Brain activity signals and motion data
were collected simultaneously using electroencephalography (EEG) and inertial
measurement units (IMUs). Results: The whole procedure took about 2 h, during
which around 30 min were spent on walking tasks, involving 35 complete tours
in the designed 8‑m hallway by pwPD. Both EEG and IMUs sensor data could
be collected, accompanied by FOG episode data marked by the neurologist. The
video recordings of the patient’s walking tasks were checked and reanalyzed by
the neurologist sometime after the data experiment for marking the beginnings
and ends of the observed FOG episodes more precisely. In the end, 24 stops were
marked as FOG, which corresponded to 11% of the sensor data collected during
the walking tasks. Conclusion: The designed FOG‑evoking task protocol could be
performed without any adverse effects, and it created enough FOG episodes for
analysis. EEG and motion sensor data could be successfully collected without any
significant artifacts