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    Detection of Freezing of Gait Episodes in Patients with Parkinson's Disease using Electroencephalography and Motion Sensors: A Protocol and its Feasibility Results
    (Wolters Kluwer Medknow Publications, 2022) Eliiyi, Ugur; Kahraman, Turhan; Genc, Arzu; Keskinoglu, Pembe; Ozkurt, Ahmet; Donmez, BerrilColakoglu
    Objective: 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.
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    Determination of freezing of gait with wearable sensors in patients with Parkinson's disease
    (Wiley, 2020) Eliiyi, Uğur; Keskinoğlu, Pembe; Kahraman, Turhan; Özkurt, Ahmet; Yürdem, Betül; Duran, G.; Genç, Aslı
    Objective: The aim was to determine freezing of gait (FOG) with wearable sensors in patients with Parkinson’s disease (PD). Background: PD is a neurodegenerative disorder leading to deficits in automatic motor performance. FOG is a major mobility problem for patients with PD, can be accompanied by postural instability and subsequent falls. Accurate and automatic FOG detection are essential for long-term symptom monitoring or preventing FOG via cueing. Although some studies have investigated the use of wearable sensors to detect FOG, conducted mostly with participants who were mainly in early stages of PD, there is no firm consensus regarding appropriate methodologies. Method: This study had a diagnostic accuracy design. Multi-segmental acceleration data was obtained from 12 patients with PD performing standardized tasks, and clinical assessment of FOG was performed by an experienced neurologist in real time and from video recordings. Three-axis wireless accelerometers were attached to patients’ ankles, waist and wrists. Trials were performed during the drug-free period, at least 12 hours after taking medication. The standardized tasks included standing from a chair, walking, 180o and 360o turnings, and passing a doorway. Trials were repeated at least 3 times, and up to 5 times if there was no FOG event. Sensor signals were processed as numerical data. Results: The mean age of patients was 64 years, 58% of them were males, and mean of disease duration was 10 years. Modified Hoehn and Yahr scale scores ranged from 2.5 to 4. The data matrix dimension is 103,261*18. Random forest (RF), artificial neural network (ANN), and decision tree (DT) methods, which are among the supervised learning algorithms, were used to predict FOG in the presence of misleading tremors on this big data. Algorithms’ performances were AUCRF/ANN/DT= 0.985 / 0.962 / 0.765 and sensitivityRF/ANN/DT= 97.1% / 94.3% / 93.8%. Conclusion: The predictions of ANN and RF were much better, while the sensitivity of DT was close to other methods. FOG detection is important to prevent it before occurring and decrease its effects. In this study, it has been shown that FOG can be detected by using the proposed algorithms with data collected from wearable sensors in patients with PD, even who are in late stages of PD.