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

doi:10.1109/EMBC44109.2020.9175288

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 proceeding › Conference contribution › Academic › peer-review

20 Citations (Scopus)

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 language	English
Title of host publication	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Publisher	Institute of Electrical and Electronics Engineers
Pages	847-850
Number of pages	4
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175288
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

Conference	42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Abbreviated title	EMBC 2020
Country/Territory	Canada
City	Montreal
Period	20/07/20 → 24/07/20

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10.1109/EMBC44109.2020.9175288

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Wang, Y., Beuving, F., Nonnekes, J., Cohen, M. X., Long, X., Aarts, R. M., & Van Wezel, R. (2020). Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (pp. 847-850). Article 9175288 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC44109.2020.9175288

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

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.",

author = "Ying Wang and Floris Beuving and Jorik Nonnekes and Cohen, {Mike X.} and Xi Long and Aarts, {Ronald M.} and {Van Wezel}, Richard",

year = "2020",

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doi = "10.1109/EMBC44109.2020.9175288",

language = "English",

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booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",

publisher = "Institute of Electrical and Electronics Engineers",

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Wang, Y, Beuving, F, Nonnekes, J, Cohen, MX, Long, X , Aarts, RM & Van Wezel, R 2020, Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society., 9175288, Institute of Electrical and Electronics Engineers, pp. 847-850, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada, 20/07/20. https://doi.org/10.1109/EMBC44109.2020.9175288

Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals. / Wang, Ying; Beuving, Floris; Nonnekes, Jorik et al.
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. Institute of Electrical and Electronics Engineers, 2020. p. 847-850 9175288.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Beuving, Floris

AU - Nonnekes, Jorik

AU - Cohen, Mike X.

AU - Long, Xi

AU - Aarts, Ronald M.

AU - Van Wezel, Richard

N1 - Conference code: 42

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N2 - 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.

AB - 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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Wang Y, Beuving F, Nonnekes J, Cohen MX, Long X , Aarts RM et al. Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. Institute of Electrical and Electronics Engineers. 2020. p. 847-850. 9175288 doi: 10.1109/EMBC44109.2020.9175288

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

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

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