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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.

Original languageEnglish
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PublisherInstitute of Electrical and Electronics Engineers
Pages847-850
Number of pages4
ISBN (Electronic)9781728119908
DOIs
Publication statusPublished - Jul 2020
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period20/07/2024/07/20

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