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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.
|Title of host publication||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||4|
|Publication status||Published - Jul 2020|
|Event||42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada|
Duration: 20 Jul 2020 → 24 Jul 2020
Conference number: 42
|Conference||42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020|
|Abbreviated title||EMBC 2020|
|Period||20/07/20 → 24/07/20|
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