Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study
محورهای موضوعی :Maryam Allah Bakhshi 1 , Aylar Sadri 2 , Seyed Omid Shahdi 3
1 - Department of Biomedical Engineering Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Biomedical Engineering Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 - Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
کلید واژه: Parkinson's Disease(PD), Electroencephalogram (EEG) Signals, Machine Learning(ML), Support Vector Machine (SVM), Classification.,
چکیده مقاله :
Parkinson's disease is a widespread neurodegenerative condition necessitating early diagnosis for effective intervention. This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of EEG signals, employing a Support Vector Machine (SVM) classification model. this research presents novel contributions to enhance diagnostic accuracy and reliability. Our approach incorporates a comprehensive review of EEG signal analysis techniques and machine learning methods. Drawing from recent studies, we have engineered an advanced SVM-based model optimized for Parkinson's disease diagnosis. Utilizing cutting-edge feature engineering, extensive hyperparameter tuning, and kernel selection, our method achieves not only heightened diagnostic accuracy but also emphasizes model interpretability, catering to both clinicians and researchers. our method's performance is evaluated through experiments on a diverse dataset of EEG recordings from Parkinson's patients and healthy controls, showing enhanced diagnostic accuracy over conventional methods. In conclusion, this paper introduces an innovative SVM-based approach for diagnosing Parkinson's disease from EEG signals. Building upon the IEEE framework and previous research, its novelty lies in the capacity to enhance diagnostic accuracy while upholding interpretability and ethical considerations for practical healthcare applications. These advances in early Parkinson's disease detection and management revolutionize care, improving patient outcomes and quality of life.
Parkinson's disease is a widespread neurodegenerative condition necessitating early diagnosis for effective intervention. This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of EEG signals, employing a Support Vector Machine (SVM) classification model. this research presents novel contributions to enhance diagnostic accuracy and reliability. Our approach incorporates a comprehensive review of EEG signal analysis techniques and machine learning methods. Drawing from recent studies, we have engineered an advanced SVM-based model optimized for Parkinson's disease diagnosis. Utilizing cutting-edge feature engineering, extensive hyperparameter tuning, and kernel selection, our method achieves not only heightened diagnostic accuracy but also emphasizes model interpretability, catering to both clinicians and researchers. our method's performance is evaluated through experiments on a diverse dataset of EEG recordings from Parkinson's patients and healthy controls, showing enhanced diagnostic accuracy over conventional methods. In conclusion, this paper introduces an innovative SVM-based approach for diagnosing Parkinson's disease from EEG signals. Building upon the IEEE framework and previous research, its novelty lies in the capacity to enhance diagnostic accuracy while upholding interpretability and ethical considerations for practical healthcare applications. These advances in early Parkinson's disease detection and management revolutionize care, improving patient outcomes and quality of life.
[1] De Oliveira, A. P. S., et al. (2020). "Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence." Research on Biomedical Engineering 36: 311-331.
[2] Vanegas, M. I., et al. (2018). Machine learning for EEG-based biomarkers in Parkinson’s disease. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.
[3] Saikia, A., et al. (2020). Machine Learning based Diagnostic System for Early Detection of Parkinson’s Disease. 2020 International Conference on Computational Performance Evaluation (ComPE), IEEE.K. Elissa, “Title of paper if known,” unpublished.
[4] Maitín, A. M., et al. (2020). "Machine learning approaches for detecting Parkinson’s disease from EEG analysis: a systematic review." Applied Sciences 10(23): 8662.
[5] Espinoza, A. I., et al. (2022). "A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease." Clinical Parkinsonism & Related Disorders 7: 100166.
[6] Shah, S. A. A., et al. (2020). "Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals." Neural Networks 130: 75-84.
[7] Anjum, M. F., et al. (2020). "Linear predictive coding distinguishes spectral EEG features of Parkinson's disease." Parkinsonism & related disorders 79: 79-85.
[8] Mi, T.-M., et al. (2019). "High-frequency rTMS over the supplementary motor area improves freezing of gait in Parkinson's disease: a randomized controlled trial." Parkinsonism & related disorders 68: 85-90.
[9] Maitin, A. M., et al. (2022). "Survey of machine learning techniques in the analysis of EEG signals for Parkinson’s disease: A systematic review." Applied Sciences 12(14): 6967.
[10] Kurbatskaya, A., et al. (2023). "Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study." arXiv preprint arXiv:2303.06376.
[11] Bonanni, L. (2019). "The democratic aspect of machine learning: Limitations and opportunities for Parkinson's disease." Movement Disorders 34(2): 164-166.
[12] Suuronen, I., et al. (2023). "Budget-based classification of Parkinson's disease from resting state EEG." IEEE Journal of Biomedical and Health Informatics.
[13] Betrouni, N., et al. (2019). "Electroencephalography‐based machine learning for cognitive profiling in Parkinson's disease: Preliminary results." Movement Disorders 34(2): 210-217.
[14] Aljalal, M., et al. (2022). "Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques." Scientific Reports 12(1): 22547.
[15] Aljalal, M., et al. (2022). "Parkinson’s disease detection from resting-state EEG signals using common spatial pattern, entropy, and machine learning techniques." Diagnostics 12(5): 1033.
[16] Geraedts, V. J., et al. (2018). "Clinical correlates of quantitative EEG in Parkinson disease: A systematic review." Neurology 91(19): 871-883.
[17] Geraedts, V., et al. (2021). "Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson’s Disease." Clinical Neurophysiology 132(5): 1041-1048.
[18] Hassin-Baer, S., et al. (2022). "Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning." Plos one 17(1): e0261947.
[19] Yuvaraj, R., et al. (2014). "Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study." Journal of integrative neuroscience 13(01): 89-120.
[20] Koch, M., et al. (2019). Automated machine learning for EEG-based classification of Parkinson’s disease patients. 2019 IEEE International Conference on Big Data (Big Data), IEEE.
[21] Kurbatskaya, A., et al. (2023). "Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study." arXiv preprint arXiv:2303.01389.
[22] Chaturvedi, M., et al. (2017). "Quantitative EEG (QEEG) measures differentiate Parkinson's disease (PD) patients from healthy controls (HC)." Frontiers in aging neuroscience 9: 3.
[23] Lu, J. and S. K. Sorooshyari (2023). "Machine Learning Identifies a Rat Model of Parkinson’s Disease via Sleep-Wake Electroencephalogram." Neuroscience 510: 1-8.
[24] Mamun, M., et al. (2022). Vocal feature guided detection of parkinson’s disease using machine learning algorithms. 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE.
[25] Yuvaraj, R., et al. (2014). "Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease." International Journal of Psychophysiology 94(3): 482-495.
[26] George, J. S., et al. (2013). "Dopaminergic therapy in Parkinson's disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control." NeuroImage: Clinical 3: 261-270.
[27] Swann, N. C., et al. (2018). "Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing." Journal of neural engineering 15(4): 046006.
