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

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. © 2013 IEEE

Prediction of freezing of gait using analysis of brain effective connectivity

© 2014 IEEE. Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence (PDC) were used to calculate the effective connectivity of neural networks, as the input features for systems that can detect FOG based on a Multilayer Perceptron Neural Network, as well as means for assessing the causal relationships in neurophysiological neural networks during FOG episodes. The sensitivity, specificity and accuracy obtained in subject dependent analysis were 82%, 77%, and 78%, respectively. This is a significant improvement compared to previously used methods for detecting FOG, bringing this detection system one step closer to a final version that can be used by the patients to improve their symptoms

Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

© 2017 IEEE. Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get 'stuck' to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG

Optimization of head movement recognition using augmented radial basis function neural network

For people with severe spine injury, head movement recognition control has been proven to be one of the most convenient and intuitive ways to control a power wheelchair. While substantial research has been done in this area, the challenge to improve system reliability and accuracy remains due to the diversity in movement tendencies and the presence of movement artifacts. We propose a Neural-Network Configuration which we call Augmented Radial Basis Function Neural-Network (ARBF-NN). This network is constructed as a Radial Basis Function Neural-Network (RBF-NN) with a Multilayer Perceptron (MLP) augmentation layer to negate optimization limitation posed by linear classifiers in conventional RBF-NN. The RBF centroid is optimized through Regrouping Particle Swarm Optimization (RegPSO) seeded with K-Means. The trial results of ARBF-NN on Head-movement show a significant improvement on recognition accuracy up to 98.1% in sensitivity. © 2011 IEEE

Detection of Gait Initiation Failure in Parkinson's disease patients using EEG signals

© 2016 IEEE. Gait Initiation Failure (GIF) is one of the most disabling gait disturbances seen in advanced Parkinson's disease (PD). Gait Initiation is a complex motor task that requires motor and cognitive processing to enable the correct selection, timing and scaling of movement. Failure to initiate the first step often precipitates falls and leads to significant morbidity. However, the brain mechanisms underlying GIF remain unknown. This study utilized an ambulatory electroencephalography (EEG) technique to investigate the brain dynamic changes underlying GIF and aims to detect the occurrence of GIF in four PD patients. We sought to determine whether episodes of GIF might be associated with a characteristic brain signal that could be detected by surface EEG. This preliminary investigation analyzed the EEG signals through power spectra density (PSD) and centroid frequency (CF) to show that the GIF episodes were associated with significant increases in the high beta band (21-38Hz) across the central, frontal, occipital and parietal EEG sites. By implementing PSD and CF as input features with two-layer Back Propagation neural networks as a classifier, the proposed system was able to detect GIF events with a classification performance of 84.27% sensitivity and 84.80% accuracy. This is the first study to show cortical dynamic changes associated with GIF in Parkinson's disease, providing valuable information to enhance the performance of future GIF detection that could be translated into clinical practice

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. © 2012 IEEE

Abnormal patterns of theta frequency oscillations during the temporal evolution of freezing of gait in parkinson's disease

Objective: We sought to characterize the electrophysiological signature of Freezing of gait in Parkinson's disease. Methods: We examined 24 patients with idiopathic Parkinson's disease and significant freezing of gait as they performed a series of timed up-and-go tasks in their 'off' state while electroencephalographic data was collected from four scalp leads. Fast Fourier Transformation was utilized to explore the power spectral density between periods of normal walking and periods of freezing, as well as during the transition between the two states. In addition, Cross Spectrum and Cross Frequency analyses were used to explore the role of impaired temporal and spatial connectivity. Results: When compared to walking, episodes of freezing were associated with a significant increase in theta band power within the central and frontal leads. The transition from normal walking to freezing of gait was also associated with increased theta frequency coupling between the central and frontal leads, along with an increase in cross-frequency coupling in the central lead. Conclusions: Episodes of freezing of gait in Parkinson's disease are associated with abnormal oscillatory activity in the brain. Significance: These results provide novel insights into the pattern of spatiotemporal dynamics underlying freezing of gait and may provide a potential means for therapeutic prediction and alleviation of freezing episodes in susceptible patients. © 2013

Identifying montages that best detect the electroencephalogram power spectrum alteration during freezing of gait in Parkinson's disease patients

© 2016 IEEE. Our research team has previously used four Electroencephalography (EEG) leads to successfully detect and predict Freezing of Gait (FOG) in Parkinson's disease (PD). However, it remained to be determined whether these four sensor locations that were arbitrarily chosen based on their role in motor control are indeed the most optimal for FOG detection. The aim of this study was therefore to determine the most optimal location and combination of sensors to detect FOG amongst a 32-channel EEG montage using our EEG classification system. EEG measures, including power spectral density, centroid frequency and power spectral entropy, were extracted from 7 patients with PD and FOG during a series of Timed up and Go tasks. By applying a feed-forward neural networks to classify EEG data, the obtained results showed that even a small number of electrodes suffice to construct a FOG detector with expected performance, which is comparable to the use of a full 32 channels montage. This finding therefore progresses the realization of a FOG detection system that can be effectively implemented on a daily basis for FOG prevention, improving the quality of life for many patients with PD

Prediction of freezing of gait in patients with Parkinson’s disease using EEG signals

© 2018 The authors and IOS Press. All rights reserved. Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson’s disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD