The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition

Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as “feeling like their feet have been glued to the floor while trying to walk” and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75%. This results have proved the feasibility of utilized EEG in future treatment of FOG.

[1]  Nitish V. Thakor,et al.  A Subband-Based Information Measure of EEG During Brain Injury and Recovery After Cardiac Arrest , 2008, IEEE Transactions on Biomedical Engineering.

[2]  M. Hallett,et al.  Freezing of gait: moving forward on a mysterious clinical phenomenon , 2011, The Lancet Neurology.

[3]  Alois Potton,et al.  Θ , 2006, Prax. Inf.verarb. Kommun..

[4]  Ana Carolina Lorena,et al.  EEG spectro-temporal modulation energy: A new feature for automated diagnosis of Alzheimer's disease , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Nir Giladi,et al.  Objective detection of subtle freezing of gait episodes in Parkinson's disease , 2010, Movement disorders : official journal of the Movement Disorder Society.

[6]  Manfred Martin Hartmann,et al.  EpiScan: Online seizure detection for epilepsy monitoring units , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  J. M. Shine,et al.  The pathophysiological mechanisms underlying freezing of gait in Parkinson’s Disease , 2011, Journal of Clinical Neuroscience.

[8]  Helge B. D. Sørensen,et al.  Detection of arousals in Parkinson's disease patients , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Kaat Desloovere,et al.  Electromyographic profiles of gait prior to onset of freezing episodes in patients with Parkinson's disease. , 2004, Brain : a journal of neurology.

[10]  D. McDowell.  Foreword , 1999 .

[11]  Z. J. Wang,et al.  But not a-band EEG connectivity has implications for dual task performance in Parkinson ’ s disease q , 2010 .

[12]  M. Popovic,et al.  A simple method to assess freezing of gait in Parkinson's disease patients. , 2010, Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas.

[13]  E. Jovanov,et al.  deFOG — A real time system for detection and unfreezing of gait of Parkinson’s patients , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[15]  Jeffrey M. Hausdorff,et al.  Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom , 2010, IEEE Transactions on Information Technology in Biomedicine.

[16]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[17]  Z. J. Wang,et al.  theta, beta But not alpha-band EEG connectivity has implications for dual task performance in Parkinson's disease. , 2010, Parkinsonism & related disorders.

[18]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[19]  Jane H. Backer,et al.  The Symptom Experience of Patients with Parkinson's Disease , 2006, The Journal of neuroscience nursing : journal of the American Association of Neuroscience Nurses.