AB  - © 2017 IEEE. 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.
AU  - Ly, QT
AU  - Gilat, M
AU  - Chai, R
AU  - Martens, KAE
AU  - Georgiades, M
AU  - Naik, GR
AU  - Tran, Y
AU  - Lewis, SJG
AU  - Nguyen, HT
DA  - 2017/09/13
DO  - 10.1109/EMBC.2017.8037499
EP  - 3047
JO  - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PY  - 2017/09/13
SP  - 3044
TI  - Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals
Y1  - 2017/09/13
Y2  - 2026/05/15
ER  -