Quynh Tran Ly, AM Ardi Handojoseno, Moran Gilat, Rifai Chai, Kaylena A Ehgoetz Martens, Matthew Georgiades, Ganesh R. Naik, Yvonne Tran, Simon JG Lewis, Hung T Nguyen
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea (South), 2017, pp. 3044-3047, doi: 10.1109/EMBC.2017.8037499.
Publication year: 2017


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.