Why the study?
The study aimed to evaluate features extracted from EEG signals through HOS, Haralick descriptors, and Fractal Features as new biomarkers for Parkinson disease identification.
Does feature extraction from EEG signals using Haralick descriptors and Fractal Features accurately detect Parkinson's Disease in individuals undergoing an attentional cognitive task?
Population
50 individuals from the Open Neuro repository who underwent an attentional cognitive task
Comparison
Parkinsons subjects vs control subjects
Key result
Haralick descriptors combined with a Random Forest classifier achieved an accuracy of 79.49% in differentiating between Parkinson's patients on medication and healthy control subjects.
Authors
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EEG Haralick features may support PD biomarker exploration; leaves open prospective validation in larger cohorts.
Does feature extraction from EEG signals using Haralick descriptors and Fractal Features accurately detect Parkinson's Disease in individuals undergoing an attentional cognitive task?
p-value: p=≤0.01
Textural feature extraction from EEG signals using Haralick descriptors and Random Forest classification can differentiate Parkinson's patients on dopaminergic medication from healthy controls with 79.49% accuracy.
Souza et al. (2024) studied Parkinson's Disease (n=50). Haralick descriptors and Random Forest (RF) classifier vs. Support Vector Machine (SVM) and Fractal techniques was evaluated on Classification accuracy (ON medication vs Control) (p=≤0.01). Haralick descriptors combined with a Random Forest classifier achieved an accuracy of 79.49% in differentiating between Parkinson's patients on medication and healthy control subjects.
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