Predicting the Onset of Freezing of Gait Using EEG Dynamics

• Published in: Applied sciences 2023-01, Vol.13 (1), p.302
• Main Authors: John, Alka Rachel, Cao, Zehong, Chen, Hsiang-Ting, Martens, Kaylena Ehgoetz, Georgiades, Matthew, Gilat, Moran, Nguyen, Hung T., Lewis, Simon J. G., Lin, Chin-Teng
• Format: Article
• Language: English
• Subjects: convolutional neural network; Deep learning; EEG; EEGNet; Electroencephalography; Experiments; freezing of gait; Gait; Neural networks; Parkinson's disease; Patients; Quality of life; Shallow ConvNet; Transitions; voluntary stopping; Walking
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13010302

Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.