Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals
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SourceProceedings of the Annual Conference of the IEEE Engineering in Medicine and Biology Society, 2020, (2020), pp. 847-850
Article / Letter to editor
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Proceedings of the Annual Conference of the IEEE Engineering in Medicine and Biology Society
SubjectNeuroinformatics; Radboudumc 13: Stress-related disorders DCMN: Donders Center for Medical Neuroscience; Radboudumc 3: Disorders of movement DCMN: Donders Center for Medical Neuroscience
Parkinson's disease (PD) patients with freezing of gait (FOG) can suddenly lose their forward moving ability leading to unexpected falls. To overcome FOG and avoid the falls, a real-time accurate FOG detection or prediction system is desirable to trigger on-demand cues. In this study, we designed and implemented an in-place movement experiment for PD patients to provoke FOG and meanwhile acquired multimodal physiological signals, such as electroencephalography (EEG) and accelerometer signals. A multimodal model using brain activity from EEG and motion data from accelerometers was developed to improve FOG detection performance. In the detection of over 700 FOG episodes observed in the experiments, the multimodal model achieved 0.211 measured by Matthews Correlation Coefficient (MCC) compared with the single-modal models (0.127 or 0.139).Clinical Relevance- This is the first study to use multimodal: EEG and accelerometer signal analysis in FOG detection, and an improvement was achieved.
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