چهارشنبه 30 مهر 1404
دوره 11، شماره 3 - ( تابستان 1404 )
جلد 11 شماره 3 صفحات 222-213 |
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Ethics code: We have used a publicly available dataset to validate our proposed algorithm, hence we don’t have
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Ghavami D, Radman M, Chaibakhsh A. Optimized Time-domain Feature Extraction for Early Onset Diagnosis of Parkinson Disease From EEG Signals. Caspian J Neurol Sci 2025; 11 (3) :213-222
URL: http://cjns.gums.ac.ir/article-1-784-fa.html
URL: http://cjns.gums.ac.ir/article-1-784-fa.html
Optimized Time-domain Feature Extraction for Early Onset Diagnosis of Parkinson Disease From EEG Signals. مجله علوم اعصاب کاسپین. 1404; 11 (3) :213-222
چکیده: (425 مشاهده)
Background: Early and accurate diagnosis of Parkinson disease (PD) is essential for enhancing patients’ quality of life and enabling more effective symptom management. Brain signal analysis, a non-invasive and reliable technique, provides an alternative or complementary method to traditional diagnostic approaches.
Objectives: This study aims to develop a diagnostic method for PD by combining signal processing techniques with machine learning (ML) algorithms.
Materials & Methods: Electroencephalography (EEG) signals were initially segmented into smaller windows using a windowing technique. The intrinsic mode functions (IMFs) were subsequently derived using the empirical mode decomposition (EMD) technique. The second-order difference plot (SODP) method was applied to each IMF, and components with higher informational content were selected for feature extraction. These features were subsequently used to train a decision tree classifier. Various window lengths were evaluated to determine the optimal time window for feature extraction, with 4 seconds identified as the optimal duration.
Results: The proposed method was evaluated using the San Diego EEG dataset, which demonstrated state-of-the-art performance compared to existing studies. The classification accuracies achieved for various scenarios were as follows: 99.7% for open-eyes off–PD vs healthy controls (HCs), 96.7% for open-eyes on–PD vs HC, and 98.54% for open-eyes off–PD vs on–PD.
Conclusion: The results underscore the strong potential of the proposed method in effectively addressing key classification challenges associated with Parkinson’s disease.
Objectives: This study aims to develop a diagnostic method for PD by combining signal processing techniques with machine learning (ML) algorithms.
Materials & Methods: Electroencephalography (EEG) signals were initially segmented into smaller windows using a windowing technique. The intrinsic mode functions (IMFs) were subsequently derived using the empirical mode decomposition (EMD) technique. The second-order difference plot (SODP) method was applied to each IMF, and components with higher informational content were selected for feature extraction. These features were subsequently used to train a decision tree classifier. Various window lengths were evaluated to determine the optimal time window for feature extraction, with 4 seconds identified as the optimal duration.
Results: The proposed method was evaluated using the San Diego EEG dataset, which demonstrated state-of-the-art performance compared to existing studies. The classification accuracies achieved for various scenarios were as follows: 99.7% for open-eyes off–PD vs healthy controls (HCs), 96.7% for open-eyes on–PD vs HC, and 98.54% for open-eyes off–PD vs on–PD.
Conclusion: The results underscore the strong potential of the proposed method in effectively addressing key classification challenges associated with Parkinson’s disease.
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