Abstract
This research paper examines the use of Electroencephalogram (EEG) signal feature extraction for diagnosing neurological disorders, specifically Alzheimer's, Parkinson's, and seizure disorders. It evaluates various methods for categorizing EEG signals, including time-domain, frequency-domain, and statistical transformations emphasizing their effectiveness in distinguishing relevant brainwave patterns (beta, alpha, theta, delta) from artifacts like eye blinks and muscle movements. The study highlights the challenges in artifact removal and provides an overview of key feature extraction techniques, particularly in the time and frequency domains. The implementation section details the application of machine learning algorithms to classify mental states using statistical features from EEG signals. The research identifies specific EEG patterns associated with Alzheimer's, Parkinson's, and seizure disorders, noting alterations in alpha, theta, and delta waves. The paper underscores the critical role of EEG feature extraction in diagnosing neurological disorders and recommends incorporating additional frequency-based methods to enhance predictive accuracy in future research.
Keywords
Electroencephalogram (EEG), Signal processing, Neurological disorders, Feature extraction, Deep learning, Time-frequency methods,Downloads
References
- F. Castro, J. Melgarejo, C.A. Chavez, G.A. De Erausquin, J.D. Terwilliger, J.H. Lee, G.E. Maestre, Total plasma homocysteine and depressive symptoms in older Hispanics. Journal of Alzheimer's Disease, 82(s1), (2021) S263-S269. https://doi.org/10.3233/JAD-201062
- G. Melikishvili, T. Bienvenu, N. Tabatadze, T. Gachechiladze, E. Kurua, S. Gverdtsiteli, M. Melikishvili, O. Dulac, Novel UBE3A pathogenic variant in a large Georgian family produces non-convulsive status epilepticus responsive to ketogenic diet. Seizure. 94, (2022) 70-73. https://doi.org/10.1016/j.seizure.2021.11.012
- B. Gosala, P.D. Kapgate, P. Jain, R. Chaurasia, M. Gupta, Wavelet transforms for feature engineering in EEG data processing: An application on Schizophrenia. Biomedical Signal Processing and Control, 85, (2023) 104811. https://doi.org/10.1016/j.bspc.2023.104811
- S.J.J. Jui, R.C. Deo, P.D. Barua, A. Devi, J. Soar, U.R. Acharya, Application of entropy for automated detection of neurological disorders with electroencephalogram signals: a review of the last decade (2012-2022). IEEE Access, (2023) 71905-71924. https://doi.org/10.1109/ACCESS.2023.3294473
- A. Montenegro, G. Sosa, N. Figueroa, V. Vargas, H. Franco, Evaluation of stabilometry descriptors for human balance function classification using diagnostic and statokinesigram data. Biomedical Signal Processing and Control, 84, (2023) 104861. https://doi.org/10.1016/j.bspc.2023.104861
- J. Li, W. Pan, H. Huang, J. Pan, F. Wang, STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition. Frontiers in Human Neuroscience, 17, (2023) 1169949. https://doi.org/10.3389/fnhum.2023.1169949
- H. Yu, S. Baek, J. Lee, I. Sohn, B. Hwang, C. Park, Deep Neural Network-based Empirical Mode Decomposition for Motor Imagery EEG Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE, (2024). https://doi.org/10.1109/TNSRE.2024.3432102
- S.Y. Shah, H. Larijani, R.M. Gibson, D. Liarokapis, Epileptic seizure classification based on random neural networks using discrete wavelet transform for electroencephalogram signal decomposition. Applied Sciences, 14(2), (2024) 599.https://doi.org/10.3390/app14020599
- R. Koliqi, A. Fathima, A.K. Tripathi, N. Sohi, R.E. Jesudasan, C. Mahapatra, Innovative and Effective Machine Learning-Based Method to Analyze Alcoholic Brain Activity with Nonlinear Dynamics and Electroencephalography Data. SN Computer Science, 5(1), (2023) 113. https://doi.org/10.1007/s42979-023-02424-6
- M, Ragavi, M.K. Subha Sri Lakshmi, Automation by Brain Sense through Eeg Waves. International Research Journal of Multidisciplinary Technovation, 2(4), (2020) 1-3. https://doi.org/10.34256/irjmt2041
- S.A. Asha, C. Sudalaimani, P. Devanand, G. Alexander, A.M. Lathikakumari, S.V. Thomas, R.N. Menon, Analysis of EEG microstates as biomarkers in neuropsychological processes–Review. Computers in Biology and Medicine, 173, (2024) 108266. https://doi.org/10.1016/j.compbiomed.2024.108266
- S. Liu, J. Wang, S. Li, L. Cai, Epileptic seizure detection and prediction in EEGS using power spectra density parameterization. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31, (2023) 3884 - 3894. https://doi.org/10.1109/TNSRE.2023.3317093
- Y. Ezazi, P. Ghaderyan, A new Cepstral-based biomarker of reward positivity evaluated in Parkinson’s disease detection. Tabriz Journal of Electrical Engineering, (2024). https://doi.org/10.22034/tjee.2024.60087.4798
- Y. Wang, C. Song, T. Zhang, Z. Yao, Z. Chang, D. Wang, Feature Extraction of Motor Imagery EEG via Discrete Wavelet Transform and Generalized Maximum Fuzzy Membership Difference Entropy: A Comparative Study. Electronics, 12(10), (2023) 2207. https://doi.org/10.3390/electronics12102207
- A. Maturana-Candelas, C. Gómez, J. Poza,S.J. Ruiz-Gómez, R. Hornero, Inter-band Bispectral Analysis of EEG Background Activity to Characterize Alzheimer's Disease Continuum. Frontiers in Computational Neuroscience, 14, (2020) 70. https://doi.org/10.3389/fncom.2020.00070
- B.K. Gulay, N. Demirel, A. Vahaplar, C. Guducu, A novel feature extraction method using chemosensory EEG for Parkinson's disease classification. Biomedical Signal Processing and Control, 79(2), (2023) 104147. https://doi.org/10.1016/j.bspc.2022.104147
- F.R. Farina, D.D. Emek-Savaş, L. Rueda-Delgado, R. Boyle, H. Kiiski, G. Yener, R. Whelan, A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage. 215 (2020) 116795. https://doi.org/10.1016/j.neuroimage.2020.116795
- L.V. Tran, H.M. Tran, T.M. Le, T.T.M. Huynh, H.T. Tran, S.V.T. Dao, Application of Machine Learning in Epileptic Seizure Detection. Diagnostics (Basel), 12(11), (2022) 2879. https://doi.org/10.3390/diagnostics12112879
- R. Naily, S. Yahia, M. Zaied, A New Deep Learning Architecture Based on LSTM and Wavelet Transform for Epileptic EEG Signal Classification. International Conference on Intelligent Systems Design and Applications, (2023), 353-362. https://doi.org/10.1007/978-3-031-64813-7_36
- I. Ahmad, X. Wang, D. Javeed, P. Kumar, O.W. Samuel, S. Chen, (2023) A hybrid deep learning approach for epileptic seizure detection in EEG signals. IEEE Journal of Biomedical and Health Informatics, IEEE, 1-12. https://doi.org/ 10.1109/JBHI.2023.3265983
- K. Baik, J.H. Jung, S.H. Jeong, S.J. Chung, H.S. Yoo, P.H. Lee, Y.H. Sohn, S.W. Kang, B.S. Ye, Implication of EEG theta/alpha and theta/beta ratio in Alzheimer's and Lewy body disease. scientific reports, 12(1), (2022) 18706. https://doi.org/10.1038/s41598-022-21951-5
- M. Ljubicic, S. Sare, I. Kolcic, Sleep Quality and Evening Salivary Cortisol Levels in Association with the Psychological Resources of Parents of Children with Developmental Disorders and Type 1 Diabetes. Journal of autism and developmental disorders, (2024) 1-4. https://doi.org/10.1007/s10803-024-06269-7
- M. Zhu, A. HajiHosseini, T.R. Baumeister, S. Garg, S. Appel-Cresswell, M.J. McKeown, Altered EEG alpha and theta oscillations characterize apathy in Parkinson's disease during incentivized movement. NeuroImage: Clinical, 23, (2019) 101922. https://doi.org/10.1016/j.nicl.2019.101922