Evaluating the Different Stages of Parkinson’s Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis

Abstract

Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson’s disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated “Bag” with the best accuracy of 0.90, followed by “LogitBoost” with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.

CCZO Residual GhostNet

“…Here, in the randomization phase, the chaotic chebychev randomization is incorporated with the foraging behaviour of the zebra for enhancing the exploration strategy to obtain the global best solution. The expression that represents the chaotic chebyshev randomization is expressed as: (8) (9) (10) Thus, using the equation (10), the solution updation is devised using the CCZO algorithm and assist to obtain the global best solution.…”

“…Techniques based on biomarkers include quantifying biological markers found in different parts of the body and blood to provide information on the existence and severity of illness. Another potential diagnostic method for PD is electroencephalography (EEG) [10]. EEG-based treatments have several benefits with respect to other diagnostic techniques, such as cost-effectiveness, non-interfering, and better resolution, as they are non-invasive.…”

Ensemble classifiers fed by functional connectivity during cognitive processing differentiate Parkinson’s disease even being under medication

“…Across years of research, various EEG characteristics fed ML/DL models to predict PD with different cognitive stages (Maitín et al 2020). A large majority of these studies used several resting state features for their classification models (Yuvaraj et al 2018, Chaturvedi et al 2017, Oh et al 2020, Anjum et al 2020, Aljalal et al 2022, Chang et al 2022, Yang and Huang 2022, Qiu et al 2022. Moreover, the sleep data of PD individuals were also used to detect different kinds of clinical states of PD (Zhang et al 2022).…”

Hook Fabric Electroencephalography Electrode for Brain Activity Measurement without Shaving the Head

“…EEG is used to diagnose brain-related seizures and inflammation, including epilepsy, Parkinson’s disease [ 6 ], and stroke [ 7 ]. The application of EEG in brain–computer interfaces [ 8 ], sleep disorder diagnosis [ 9 ], and cognitive neuroscience [ 10 ] is booming.…”