Early Identification of Parkinson's Disease Using Time Frequency Analysis on EEG Signals
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Abstract
Parkinson's Disease (PD) is a progressive neurological disorder. It affects movement and can significantly impact quality of life. Early and accurate diagnosis is crucial for effective management and intervention. Traditional diagnostic methods can be time-consuming and less effective in the early stages of the disease. This study aims to develop an automated approach for identifying PD using time-frequency image analysis of electroencephalogram (EEG) signals. The goal is to enhance diagnostic accuracy and efficiency, facilitating early detection. EEG signals, often contaminated with artifacts such as eye blinks and muscle movements etc., were first cleaned. Time-frequency images were then plotted from the cleaned signals, and Event-Related Spectral Perturbation (ERSP) plots were extracted. A customized deep learning model was employed to classify the ERSP plots, distinguishing PD patients from healthy controls. The deep learning model achieved an accuracy of 94.64% in separating PD patients from healthy controls. The approach demonstrated robustness against common EEG artifacts, ensuring reliable PD detection. The model's architecture was specifically designed to handle the complexities of EEG data, making it a powerful tool for PD classifications. This study highlights the potential of integrating deep learning with EEG analysis to explore PD diagnosis. The proposed method is faster and more accurate than traditional approaches, enabling early detection and timely intervention. By reducing the time required for analysis and enhancing diagnostic accuracy, this approach can significantly improve patient outcomes and support better management of Parkinson's Disease.
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References
A. S. Olagunjua and F. A., “Mitochondrial dysfunction: A notable contributor to the progression of Alzheimer's and Parkinson's disease,” Heliyon, vol. 9, no. 3, 2023, doi: 10.1016/j.heliyon.2023.e14387
M. M. Alvarez et al., “A comprehensive approach to Parkinson’s disease: addressing its molecular, clinical, and therapeutic aspects,” International Journal of Molecular Sciences, vol. 25, no. 13, p. 7183, Jun. 2024, doi: 10.3390/ijms25137183.
S., J., R., V., B. and M. R, “Neuroanatomy, Substantia Nigra,” StatPearls Publishing, 2022
J. H. and K. K., “Clinical and imaging evidence of brain-first and body-first Parkinson's disease,” Neurobiology of Disease, vol. 164, 2022, doi: https://doi.org/10.1016/j.nbd.2022.105626
S. V. and T. R.-F, “Clinical Diagnostic Accuracy of Parkinson's Disease: Where Do We Stand?,” Movement Disorders, vol. 38, no. 4, 2023, doi: https://doi.org/10.1002/mds.29317
A. S., “Biomarkers in Parkinson’s Disease. Towards translating research to clinical practice”, Neuromethods, vol. 173, 2022, doi: https://doi.org/10.1007/9
P. M. -C. and T. V, “Computation of the electroencephalogram (EEG) from network models of point neurons,” PLOS Computational Biology, vol. 17, no. 4, 2021, doi: https://doi.org/10.1371/journal.pcbi.1008893
E. M. Kline and M. C, “Genetic and Environmental Factors in Parkinson's Disease Converge on Immune Function and Inflammation,” Movement Disorders, vol. 36, no. 1, 2021, doi: https://doi.org/10.1002/mds.28411
Y. J. Bae et al., “Imaging the substantia nigra in Parkinson disease and other parkinsonian syndromes,” Radiology, vol. 300, no. 2, pp. 260–278, Jun. 2021, doi: 10.1148/radiol.2021203341.
T. Mortezazadeh, H. Seyedarabi, B. Mahmoudian, and J. P. Islamian, “Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges,” The Egyptian Journal of Radiology and Nuclear Medicine, vol. 52, no. 1, Mar. 2021, doi: 10.1186/s43055-021-00454-9.
E. Samson and M. D. Noseworthy, “A review of diagnostic imaging approaches to assessing Parkinson’s disease,” Brain Disorders, vol. 6, p. 100037, May 2022, doi: 10.1016/j.dscb.2022.100037.
M. Zubelzu, T. Morera-Herreras, G. Irastorza, J. C. Gomez-Esteban, and A. Murueta-Goyena, “Plasma and serum alpha-synuclein as a biomarker in Parkinson’s disease: A meta-analysis,” Parkinsonism & Related Disorders, vol. 99, pp. 107–115, Jun. 2022, doi: 10.1016/j.parkreldis.2022.06.001.
T. D. De Carvalho Costa et al., “Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson’s disease?,” Neurophysiologie Clinique, vol. 53, no. 1, p. 102839, Jan. 2023, doi: 10.1016/j.neucli.2022.102839.
A. P. and M. B., “Study of EEG microstates in Parkinson’s disease: a potential biomarker?,” Cognitive Neurodynamics, vol. 15, 2021, doi: https://doi.org/10.1007/s11571-020-09643-0
G. Gimenez-Aparisi, E. Guijarro-Estelles, A. Chornet-Lurbe, S. Ballesta-Martinez, M. Pardo-Hernandez, and Y. Ye-Lin, “Early detection of Parkinson’s disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography,” Heliyon, vol. 9, no. 10, p. e20625, Oct. 2023, doi: 10.1016/j.heliyon.2023.e20625.
J. Cao et al., “Brain functional and effective connectivity based on electroencephalography recordings: A review,” Human Brain Mapping, vol. 43, no. 2, pp. 860–879, Oct. 2021, doi: 10.1002/hbm.25683.
S. Wang, G. Wang, G. Pei and T. Yan, "An EEG-based approach for Parkinson’s disease diagnosis using capsule network," 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 2022, pp. 1641-1645, doi: 10.1109/ICSP54964.2022.9778541.
M. H. Aslam. and S. M. Usman, “Classification of EEG Signals for Prediction of Epileptic Seizure,” Applied Sciences, vol. 12, no. 14, 2022, doi: https://doi.org/10.3390/app12147251
A. H. Meghdadi et al., “EEG and ERP biosignatures of mild cognitive impairment for longitudinal monitoring of early cognitive decline in Alzheimer’s disease,” PLoS ONE, vol. 19, no. 8, p. e0308137, Aug. 2024, doi: 10.1371/journal.pone.0308137.
N. Darcy et al., “Spectral and spatial distribution of subthalamic beta peak activity in Parkinson’s disease patients,” Experimental Neurology, vol. 356, p. 114150, Jun. 2022, doi: 10.1016/j.expneurol.2022.114150.
A. M. Maitin, J. P. R. Munoz, and Á. J. Garcia-Tejedor, “Survey of Machine learning techniques in the analysis of EEG signals for Parkinson’s Disease: A Systematic review,” Applied Sciences, vol. 12, no. 14, p. 6967, Jul. 2022, doi: 10.3390/app12146967.
V. R. Ferreira et al., “Capturing the attentional response to clinical auditory alarms: An ERP study on
priority pulses,” PLoS ONE, vol. 18, no. 2, p. e0281680, Feb. 2023, doi: 10.1371/journal.pone.0281680.
S. Morales and M. E. Bowers, “Time-frequency analysis methods and their application in developmental EEG data,” Developmental Cognitive Neuroscience, vol. 54, p. 101067, Jan. 2022, doi: 10.1016/j.dcn.2022.101067.
G. M. Meyer et al., “Electrophysiological underpinnings of reward processing: Are we exploiting the full potential of EEG,” NeuroImage, vol. 242, 2021, doi: https://doi.org/10.1016/j.neuroimage.2021.118478
D. La Rocca, H. Wendt, V. Van Wassenhove, P. Ciuciu, and P. Abry, “Revisiting functional connectivity for infraslow Scale-Free brain dynamics using complex wavelets,” Frontiers in Physiology, vol. 11, Jan. 2021, doi: 10.3389/fphys.2020.578537.
A. Keil et al., “Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series,” Psychophysiology, vol. 59, no. 5, Apr. 2022, doi: 10.1111/psyp.14052.
L. Li, J. Luo, Y. Li, L. Zhang, and Y. Guo, “Phase analysis of Event-Related potentials based on dynamic mode decomposition,” Mathematics, vol. 10, no. 23, p. 4406, Nov. 2022, doi: 10.3390/math10234406.
B. J. Roach and D. H. Mathalon, “Event-Related EEG Time-Frequency Analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia,” Schizophrenia Bulletin, vol. 34, no. 5, pp. 907–926, Jul. 2008, doi: 10.1093/schbul/sbn093.
D. Mustafa, Z. Yicheng, G. Minjie, H. Jonas, and F. Jurgen, “Motor Current Based Misalignment Diagnosis on Linear Axes with Short-Time Fourier Transform (STFT),” Procedia CIRP, vol. 106, pp. 239-243, Jan. 2022, doi: 10.1016/j.procir.2022.02.185.
H. Dong, G. Yu, T. Lin, and Y. Li, “An energy-concentrated wavelet transform for time-frequency analysis of transient signal,” Signal Processing, vol. 206, p. 108934, Jan. 2023, doi: 10.1016/j.sigpro.2023.108934.
J. Y. Chen and B. Z. Li, “The short-time Wigner-Ville distribution,” Signal Processing, vol. 219, p. 109402, Jan. 2024, doi: 10.1016/j.sigpro.2024.109402.
Z. Ye, M. Heldmann, L. Herrmann, N. Bruggemann, and T. F. Munte, “Altered alpha and theta oscillations correlate with sequential working memory in Parkinson’s disease,” Brain Communications, vol. 4, no. 3, Apr. 2022, doi: 10.1093/braincomms/fcac096.
P.-L. Chen et al., “Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson’s disease,” Frontiers in Human Neuroscience, vol. 16, Sep. 2022, doi: 10.3389/fnhum.2022.958521.
D. Hunerli-Gunduz et al., “Reduced power and phase-locking values were accompanied by thalamus, putamen, and hippocampus atrophy in Parkinson’s disease with mild cognitive impairment: an event-related oscillation study,” Neurobiology of Aging, vol. 121, pp. 88–106, Oct. 2022, doi: 10.1016/j.neurobiolaging.2022.10.001.
M. M. Taye, “Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions,” Computation, vol. 11, no. 3, p. 52, Mar. 2023, doi: 10.3390/computation11030052.
H. Zerouaoui and A. Idri, “Deep hybrid architectures for binary classification of medical breast cancer images,” Biomedical Signal Processing and Control, vol. 71, p. 103226, Oct. 2021, doi: 10.1016/j.bspc.2021.103226.
V. Tiwari, R. C. Joshi, and M. K. Dutta, “Deep neural network for multi-class classification of medicinal plant leaves,” Expert Systems, vol. 39, no. 8, May 2022, doi: 10.1111/exsy.13041.
A. Zaidi and A. S. M. A. Luhayb, “Two statistical approaches to justify the use of the logistic function in binary logistic regression,” Mathematical Problems in Engineering, vol. 2023, no. 1, Jan. 2023, doi: 10.1155/2023/5525675.
N. D. Odera and N. G. Odiaga, “A comparative analysis of recurrent neural network and support vector machine for binary classification of spam short message service,” World Journal of Advanced Engineering Technology and Sciences, vol. 9, no. 1, pp. 127–152, May 2023, doi: 10.30574/wjaets.2023.9.1.0142.
S. Sharma and K. Guleria, “Deep learning models for image Classification: comparison and applications,” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Apr. 2022, doi: 10.1109/icacite53722.2022.9823516.
E. Gibson, N. J. Lobaugh, S. Joordens, and A. R. McIntosh, “EEG variability: Task-driven or subject-driven signal of interest?,” NeuroImage, vol. 252, p. 119034, Mar. 2022, doi: 10.1016/j.neuroimage.2022.119034.
A. M. Roy, “An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces,” Biomedical Signal Processing and Control, vol. 74, p. 103496, Jan. 2022, doi: 10.1016/j.bspc.2022.103496.
R. Zhang, J. Jia, and R. Zhang, “EEG analysis of Parkinson’s disease using time–frequency analysis and deep learning,” Biomedical Signal Processing and Control, vol. 78, p. 103883, Jun. 2022, doi: 10.1016/j.bspc.2022.103883.
S. L. Oh et al., “A deep learning approach for Parkinson’s disease diagnosis from EEG signals,” Neural Computing and Applications, vol. 32, no. 15, pp. 10927–10933, Aug. 2018, doi: 10.1007/s00521-018-3689-5.
M. Shaban and A. W. Amara, “Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease,” PLoS ONE, vol. 17, no. 2, p. e0263159, Feb. 2022, doi: 10.1371/journal.pone.0263159.
S. Siuly, S. K. Khare, E. Kabir, M. T. Sadiq, and H. Wang, “An efficient Parkinson’s disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network,” Computers in Biology and Medicine, vol. 174, p. 108462, Apr. 2024, doi: 10.1016/j.compbiomed.2024.108462.
C.-Y. Yang and Y.-Z. Huang, “Parkinson’s disease Classification using Machine learning approaches and Resting-State EEG,” Journal of Medical and Biological Engineering, vol. 42, no. 2, pp. 263–270, Apr. 2022, doi: 10.1007/s40846-022-00695-7.
S. Xu et al., “Retraction to using a deep recurrent neural network with EEG signal to detect Parkinson’s disease,” Annals of Translational Medicine, vol. 9, no. 17, p. 1396, Sep. 2021, doi: 10.21037/atm-2021-25.
M. F. Anjum, S. Dasgupta, R. Mudumbai, A. Singh, J. F. Cavanagh, and N. S. Narayanan, “Linear predictive coding distinguishes spectral EEG features of Parkinson’s disease,” Parkinsonism & Related Disorders, vol. 79, pp. 79–85, Aug. 2020, doi: 10.1016/j.parkreldis.2020.08.001.