Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

Q. T. Ly; A. M. A. Handojoseno; M. Gilat; R. Chai; K. A. E. Martens; M. Georgiades; G. R. Naik; Y. Tran; S. J. G. Lewis; H. T. Nguyen

doi:10.1109/EMBC.2017.8037499

Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

Q. T. Ly, A. M. A. Handojoseno, M. Gilat, R. Chai, K. A. E. Martens, M. Georgiades, G. R. Naik, Y. Tran, S. J. G. Lewis, H. T. Nguyen

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

30 Citations (Scopus)

Abstract

![CDATA[Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.]]

Original language	English
Title of host publication	Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea
Publisher	IEEE
Pages	3044-3047
Number of pages	4
ISBN (Print)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037499
Publication status	Published - 2017
Event	IEEE Engineering in Medicine and Biology Society. Annual International Conference - Duration: 11 Jul 2022 → …

Publication series

Name
ISSN (Print)	1557-170X

Conference

Conference	IEEE Engineering in Medicine and Biology Society. Annual International Conference
Period	11/07/22 → …

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10.1109/EMBC.2017.8037499

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Ly, Q. T., Handojoseno, A. M. A., Gilat, M., Chai, R., Martens, K. A. E., Georgiades, M., Naik, G. R., Tran, Y., Lewis, S. J. G., & Nguyen, H. T. (2017). Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. In Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea (pp. 3044-3047). IEEE. https://doi.org/10.1109/EMBC.2017.8037499

Ly, Q. T. ; Handojoseno, A. M. A. ; Gilat, M. et al. / Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea. IEEE, 2017. pp. 3044-3047

@inproceedings{2fcc82049dee46cf940d3156029408a5,

title = "Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals",

abstract = "![CDATA[Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.]]",

author = "Ly, {Q. T.} and Handojoseno, {A. M. A.} and M. Gilat and R. Chai and Martens, {K. A. E.} and M. Georgiades and Naik, {G. R.} and Y. Tran and Lewis, {S. J. G.} and Nguyen, {H. T.}",

year = "2017",

doi = "10.1109/EMBC.2017.8037499",

language = "English",

isbn = "9781509028092",

publisher = "IEEE",

pages = "3044--3047",

booktitle = "Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea",

note = "IEEE Engineering in Medicine and Biology Society. Annual International Conference ; Conference date: 11-07-2022",

}

Ly, QT, Handojoseno, AMA, Gilat, M, Chai, R, Martens, KAE, Georgiades, M, Naik, GR, Tran, Y, Lewis, SJG & Nguyen, HT 2017, Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. in Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea. IEEE, pp. 3044-3047, IEEE Engineering in Medicine and Biology Society. Annual International Conference, 11/07/22. https://doi.org/10.1109/EMBC.2017.8037499

Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. / Ly, Q. T.; Handojoseno, A. M. A.; Gilat, M. et al.
Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea. IEEE, 2017. p. 3044-3047.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

AU - Ly, Q. T.

AU - Handojoseno, A. M. A.

AU - Gilat, M.

AU - Chai, R.

AU - Martens, K. A. E.

AU - Georgiades, M.

AU - Naik, G. R.

AU - Tran, Y.

AU - Lewis, S. J. G.

AU - Nguyen, H. T.

PY - 2017

Y1 - 2017

N2 - ![CDATA[Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.]]

AB - ![CDATA[Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.]]

UR - https://hdl.handle.net/1959.7/uws:66978

U2 - 10.1109/EMBC.2017.8037499

DO - 10.1109/EMBC.2017.8037499

M3 - Conference Paper

SN - 9781509028092

SP - 3044

EP - 3047

BT - Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea

PB - IEEE

T2 - IEEE Engineering in Medicine and Biology Society. Annual International Conference

Y2 - 11 July 2022

ER -

Ly QT, Handojoseno AMA, Gilat M, Chai R, Martens KAE, Georgiades M et al. Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. In Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea. IEEE. 2017. p. 3044-3047 doi: 10.1109/EMBC.2017.8037499

Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

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