Analysis and Prediction of the Freezing of Gait Using EEG Brain Dynamics

Abstract

Freezing of Gait (FOG) is a common symptom in the advanced stages of Parkinson's disease (PD), which significantly affects patients' quality of life. Treatment options offer limited benefit and there are currently no mechanisms able to effectively detect FOG before it occurs, allowing time for a sufferer to avert a freezing episode. Electroencephalography (EEG) offers a novel technique that may be able to address this problem. In this paper, we investigated the univariate and multivariate EEG features determined by both Fourier and wavelet analysis in the confirmation and prediction of FOG. The EEG power measures and network properties from 16 patients with PD and FOG were extracted and analyzed. It was found that both power spectral density and wavelet energy could potentially act as biomarkers during FOG. Information in the frequency domain of the EEG was found to provide better discrimination of EEG signals during transition to freezing than information coded in the time domain. The performance of the FOG prediction systems improved when the information from both domains was used. This combination resulted in a sensitivity of 86.0%, specificity of 74.4%, and accuracy of 80.2% when predicting episodes of freezing, outperforming current accelerometry-based tools for the prediction of FOG.

Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease

“…Its main advantage in analyzing physiological systems is its capability to detect and analyze nonstationarity in signals and its related aspect like trends, breakdown points, and discontinuity. Wavelets are well localized in both time and frequency domain ‎[13]. In WT, we could get better low-frequency information in long time window and get high frequency in short-time window.…”

“…Later on, they found that the frequency domain information was better than time domain information in discrimination EEG signals. In this case, they used the two domains in PD detection, acquiring the combination accuracy of 80.2%  [7]. Hansen et al researched the idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) in early prediction of Parkinson's disease ‎[8].…”

Measuring prefrontal cortical activity during dual task walking in patients with Parkinson’s disease: feasibility of using a new portable fNIRS device

“…Compared to functional magnetic resonance imaging (fMRI), which has a higher spatial resolution and can reach subcortical areas, fNIRS is lightweight, easy to use, low cost, and can be portable [11, 14, 18]. Compared to electroencephalography (EEG), which can also be used during actual walking [20], fNIRS can provide higher spatial resolution, is easier to use, and more robust to head movement [14, 18, 21]. These advantages make fNIRS particularly attractive to be used for measurement during actual walking [11].…”

An EEG study of turning freeze in Parkinson's disease patients: The alteration of brain dynamic on the motor and visual cortex

“…Our group has developed a detection algorithm for recognizing FOG by analyzing energy power, entropy, correlation and brain effective connectivity (BEC) of EEG signals, providing valuable insights into the underlying brain mechanism [3], [7], [8]. However, these previous studies have focused on episodes of freezing that had mixed provocation factors.…”

“…As we have demonstrated from our previous studies, "classical" Power Spectral Density (PSD) and BEC are powerful methods for feature extraction from surface EEG recordings [7], [8]. Whilst we have established the role of PSD in analyzing FOG previously, BEC is a more recent and advanced approach, which might provide valuable insights into a better understanding of the complex physiological mechanisms of freezing in the brain.…”