Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals

Ying Wang, Floris Beuving, Jorik Nonnekes, Mike X. Cohen, Xi Long, Ronald M. Aarts, Richard Van Wezel

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
Titel42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's847-850
Aantal pagina's4
ISBN van elektronische versie9781728119908
DOI's
StatusGepubliceerd - jul 2020
Evenement42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duur: 20 jul 202024 jul 2020

Congres

Congres42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
LandCanada
StadMontreal
Periode20/07/2024/07/20

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