Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients

A. M. Ardi Handojoseno; James M. Shine; Tuan N. Nguyen; Yvonne Tran; Simon J. G. Lewis; Hung T. Nguyen

doi:10.1109/EMBC.2013.6610487

Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients

A. M. Ardi Handojoseno, James M. Shine, Tuan N. Nguyen, Yvonne Tran, Simon J. G. Lewis, Hung T. Nguyen

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

34 Citations (Scopus)

Abstract

Parkinson's Disease (PD) patients with Freezing of Gait (FOG) often experience sudden and unpredictable failure in their ability to start or continue walking, making it potentially a dangerous symptom. Emerging knowledge about brain connectivity is leading to new insights into the pathophysiology of FOG and has suggested that electroencephalogram (EEG) may offer a novel technique for understanding and predicting FOG. In this study we have integrated spatial, spectral, and temporal features of the EEG signals utilizing wavelet coefficients as our input for the Multilayer Perceptron Neural Network and k-Nearest Neighbor classifier. This approach allowed us to predict transition from walking to freezing with 87 % sensitivity and 73 % accuracy. This preliminary data affirms the functional breakdown between areas in the brain during FOG and suggests that EEG offers potential as a therapeutic strategy in advanced PD.

Original language	English
Title of host publication	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4263-4266
Number of pages	4
ISBN (Print)	9781457702167
DOIs	https://doi.org/10.1109/EMBC.2013.6610487
Publication status	Published - 2013
Externally published	Yes
Event	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan Duration: 3 Jul 2013 → 7 Jul 2013

Conference

Conference	2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/Territory	Japan
City	Osaka
Period	3/07/13 → 7/07/13

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10.1109/EMBC.2013.6610487

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Handojoseno, A. M. A., Shine, J. M., Nguyen, T. N., Tran, Y., Lewis, S. J. G., & Nguyen, H. T. (2013). Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 4263-4266). [6610487] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2013.6610487

Handojoseno, A. M. Ardi ; Shine, James M. ; Nguyen, Tuan N. ; Tran, Yvonne ; Lewis, Simon J. G. ; Nguyen, Hung T. / Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. Institute of Electrical and Electronics Engineers (IEEE), 2013. pp. 4263-4266

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title = "Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients",

abstract = "Parkinson's Disease (PD) patients with Freezing of Gait (FOG) often experience sudden and unpredictable failure in their ability to start or continue walking, making it potentially a dangerous symptom. Emerging knowledge about brain connectivity is leading to new insights into the pathophysiology of FOG and has suggested that electroencephalogram (EEG) may offer a novel technique for understanding and predicting FOG. In this study we have integrated spatial, spectral, and temporal features of the EEG signals utilizing wavelet coefficients as our input for the Multilayer Perceptron Neural Network and k-Nearest Neighbor classifier. This approach allowed us to predict transition from walking to freezing with 87 % sensitivity and 73 % accuracy. This preliminary data affirms the functional breakdown between areas in the brain during FOG and suggests that EEG offers potential as a therapeutic strategy in advanced PD.",

author = "Handojoseno, {A. M. Ardi} and Shine, {James M.} and Nguyen, {Tuan N.} and Yvonne Tran and Lewis, {Simon J. G.} and Nguyen, {Hung T.}",

year = "2013",

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language = "English",

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booktitle = "2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013",

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Handojoseno, AMA, Shine, JM, Nguyen, TN, Tran, Y, Lewis, SJG & Nguyen, HT 2013, Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients. in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013., 6610487, Institute of Electrical and Electronics Engineers (IEEE), pp. 4263-4266, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, 3/07/13. https://doi.org/10.1109/EMBC.2013.6610487

Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients. / Handojoseno, A. M. Ardi; Shine, James M.; Nguyen, Tuan N.; Tran, Yvonne; Lewis, Simon J. G.; Nguyen, Hung T.

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. Institute of Electrical and Electronics Engineers (IEEE), 2013. p. 4263-4266 6610487.

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

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AB - Parkinson's Disease (PD) patients with Freezing of Gait (FOG) often experience sudden and unpredictable failure in their ability to start or continue walking, making it potentially a dangerous symptom. Emerging knowledge about brain connectivity is leading to new insights into the pathophysiology of FOG and has suggested that electroencephalogram (EEG) may offer a novel technique for understanding and predicting FOG. In this study we have integrated spatial, spectral, and temporal features of the EEG signals utilizing wavelet coefficients as our input for the Multilayer Perceptron Neural Network and k-Nearest Neighbor classifier. This approach allowed us to predict transition from walking to freezing with 87 % sensitivity and 73 % accuracy. This preliminary data affirms the functional breakdown between areas in the brain during FOG and suggests that EEG offers potential as a therapeutic strategy in advanced PD.

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Handojoseno AMA, Shine JM, Nguyen TN, Tran Y, Lewis SJG, Nguyen HT. Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013. Institute of Electrical and Electronics Engineers (IEEE). 2013. p. 4263-4266. 6610487 https://doi.org/10.1109/EMBC.2013.6610487

Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients

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