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

^*Corresponding author for this work

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

25 Citations (Scopus)

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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 Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)
Subtitle of host publication	Enabling Innovative Technologies for Global Healthcare
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	847-850
Number of pages	4
ISBN (Electronic)	978-1-7281-1990-8
ISBN (Print)	978-1-7281-1991-5
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175288
Publication status	Published - Jul 2020
Externally published	Yes
Event	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Virtual Conference, Montreal, Canada Duration: 20 Jul 2020 → 24 Jul 2020 Conference number: 42

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher	IEEE
Volume	2020
ISSN (Print)	2375-7477
ISSN (Electronic)	2694-0604

Conference

Conference	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Abbreviated title	EMBC
Country/Territory	Canada
City	Montreal
Period	20/07/20 → 24/07/20
Other	In conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society

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10.1109/EMBC44109.2020.9175288

Cite this

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 Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020): Enabling Innovative Technologies for Global Healthcare (pp. 847-850). Article 9175288 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020). IEEE. https://doi.org/10.1109/EMBC44109.2020.9175288

Wang, Ying ; Beuving, Floris ; Nonnekes, Jorik et al. / Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals. 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020): Enabling Innovative Technologies for Global Healthcare. Piscataway, NJ : IEEE, 2020. pp. 847-850 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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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",

month = jul,

doi = "10.1109/EMBC44109.2020.9175288",

language = "English",

isbn = "978-1-7281-1991-5",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "IEEE",

pages = "847--850",

booktitle = "42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)",

address = "United States",

note = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, EMBC ; Conference date: 20-07-2020 Through 24-07-2020",

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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 Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020): Enabling Innovative Technologies for Global Healthcare., 9175288, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020, IEEE, Piscataway, NJ, pp. 847-850, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Quebec, 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 Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020): Enabling Innovative Technologies for Global Healthcare. Piscataway, NJ: IEEE, 2020. p. 847-850 9175288 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020).

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

TY - GEN

T1 - Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals

AU - Wang, Ying

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

PY - 2020/7

Y1 - 2020/7

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.

UR - https://www.scopus.com/pages/publications/85091007884

U2 - 10.1109/EMBC44109.2020.9175288

DO - 10.1109/EMBC44109.2020.9175288

M3 - Conference contribution

AN - SCOPUS:85091007884

SN - 978-1-7281-1991-5

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 847

EP - 850

BT - 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)

PB - IEEE

CY - Piscataway, NJ

T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

Y2 - 20 July 2020 through 24 July 2020

ER -

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 Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020): Enabling Innovative Technologies for Global Healthcare. Piscataway, NJ: IEEE. 2020. p. 847-850. 9175288. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). 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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