A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features

Year 2024, Volume: 28 Issue: 5, 912 - 923, 25.10.2024

Neslihan Baki , Nurhan Gürsel Özmen

https://doi.org/10.16984/saufenbilder.1367813

Abstract

Parkinson's disease (PD) is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography (EEG) recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency (MSF), Spectral Entropy (SE), Kolmogorov Algorithmic Complexity (KAC) and Weighted Symbolic Mutual Information (wSMI) were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting (GB), Gaussian Naive Bayes (GNB), and K-nearest Neighbor (KNN), Support Vector Machines (SVM), Logistic Regression (LR), Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) Algorithms. A 85% accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature.

Keywords

Parkinson, Electroencephalography, Weighted Symbolic Mutual Information (wSMI), Kolmogorov Algorithmic Complexity (KAC), Extreme gradient boosting algorithm

References

• J. Valls-Sole, F. Valldeoriola, “Neurophysiological correlate of clinical signs in parkinson’s disease,” Clinical Neurophysiology, vol. 113, no. 6, pp. 792–805, 2002.
• J. Parkinson, “An essay on the shaking palsy,” The Journal of Neuropsychiatry and Clinical Neurosciences, vol. 14, no. 2, pp. 223–236, 2002.
• I. G. McKeith, D. Galasko, K. Kosaka, E. K. Perry, D. W. Dickson, L. A. Hansen, D.P. Salmon, J. Lowe, S.S. Mirra, E.J. Byrne, G. Lennox, N.P. Quinn, J.A. Edwardson, P.G. Ince, C. Bergeron, A. Burns, B.L. Miller, S. Lovestone, D. Collerton, E.N.H. Jansen, C. Ballard, R.A.I. de Vos, G.K. Wilcock, K.A. Jellinger, R.H. Perry, “Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB),” Neurology, vol. 47, no. 5, pp. 1113–1124, 1996.
• J. Q. Trojanowski, "Neurodegeneration: The Molecular Pathology of Dementia and Movement Disorders," in D.W. Dickson (Ed.), ISN Press, Basel, 2003, pp. 11-13.
• J. Jankovic, "Pathophysiology and assessment of parkinsonian symptoms and signs," in Handbook of Parkinson’s Disease, 3rd ed., R. Pahwa, K. Lyons, and W.C. Koller, Eds. Taylor and Francis Group, LLC, New York, pp. 79-104, 2007.
• C. H. Waters, "Diagnosis and Treatment of Parkinson's Disease," translated by B. Büyükkal, Turgut Publishing and Trade Co. Ltd., Istanbul, 2000.
• H. Braak, K. Del Tredici, H. Bratzke, J. Hamm-Clement, D. Sandmann-Keil, U. Rüb “Staging of the intracerebral inclusion body pathology associated with idiopathic parkinson’s disease (preclinical and clinical stages),” Journal of Neurology, vol. 249, no. 0, pp. 1–1, 2002.
• C. Pappalettera, F. Miraglia, M. Cotelli, P. M. Rossini, F. Vecchio, “Analysis of complexity in the EEG activity of parkinson’s disease patients by means of approximate entropy,” GeroScience, vol. 44, no. 3, pp. 1599–1607, 2022.
• D. Stoffers, J. L. W. Bosboom, J. B. Deijen, E. C. Wolters, H. W. Berendse, C. J. Stam, “Slowing of oscillatory brain activity is a stable characteristic of parkinson’s disease without dementia,” Brain, vol. 130, no. 7, pp. 1847–1860, 2007.
• S. Kan, K. Satoshi, M. Akihiko, H. Motohiko, M. Tomohiko, Y. Hirokazu, Y. Mai, T. Jun, H. Kaname, “Comparison of quantitative EEGs between parkinson disease and age-adjusted normal controls,” Journal of Clinical Neurophysiology, vol. 25, no. 6, pp. 361–366, 2008.
• N. Fogelson, D. Williams, M. Tijssen, G. van Bruggen, H. Speelman, P. Brown, “Different functional loops between cerebral cortex and the subthalmic area in parkinson’s disease,” Cerebral Cortex, vol. 16, no. 1, pp. 64–75, 2006.
• E. Lalo, S. Thobois, A. Sharott, G. Polo, P. Mertens, A. Pogosyan, P. Brown, “Patterns of bidirectional communication between cortex and basal ganglia during movement in patients with parkinson disease,” The Journal of Neuroscience, vol. 28, no. 12, pp. 3008–3016, 2008.
• J. L. W. Bosboom, D. Stoffers, C.J. Stam, B.W. van Dijk, J. Verbunt, H.W. Berendse, E.Ch. Wolters, “Resting state oscillatory brain dynamics in parkinson’s disease: An MEG study,” Clinical Neurophysiology, vol. 117, no. 11, pp. 2521–2531, 2006.
• H. Tanaka, T. Koenig, R. D. Pascual-Marqui, K. Hirata, K. Kochi, D. Lehmann, “Event-related potential and EEG measures in parkinson’s disease without and with dementia,” Dementia and Geriatric Cognitive Disorders, vol. 11, no. 1, pp. 39–45, 2000.
• R. Yuvaraj, P. Thagavel J. Thomas, J. Fogarty, F. Ali, “Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings.,” Sensors. 23(2):915, vol. 23, no. 2, pp. 915, 2023.
• G. Liu, Y. Zhang, Z. Hu, X. Du, W. Wu, C. Xu, X. Wang, S. Li, “Complexity analysis of electroencephalogram dynamics in patients with parkinson’s disease,” Parkinson’s Disease, vol. 2017, pp. 1–9, 2017.
• T. M. McKenna, T. A. McMullen, M. F. Shlesinger, “The brain as a dynamic physical system,” Neuroscience, vol. 60, no. 3, pp. 587–605, 1994.
• C. Lainscsek, M. E. Hernandez, J. Weyhenmeyer, T. J. Sejnowski, H. Poizner, “Non-linear dynamical analysis of EEG time series distinguishes patients with parkinson’s disease from healthy individuals,” Frontiers in Neurology, vol. 4, 2013.
• A. M. Maitin, A. J. García-Tejedor, J. P. Munoz, “Machine learning approaches for detecting parkinson’s disease from EEG Analysis: A systematic review,” Applied Sciences, vol. 10, no. 23, p. 8662, 2020.
• M. Chaturvedi, F. Hatz, U. Gschwandtner, J. G. Bogaarts, A. Meyer, P. Fuhr, V. Roth, “Quantitative eeg (QEEG) measures differentiate parkinson’s disease (PD) patients from healthy controls (HC),” Frontiers in Aging Neuroscience, vol. 9, Jan. 2017.
• N. Betrouni, A. Delval, L. Chaton, L. Defebvre, A. Duits, A. Moonen, A. F. G. Leentjen, K. Dujardin, “Electroencephalography‐based machine learning for cognitive profiling in parkinson’s disease: Preliminary results,” Movement Disorders, vol. 34, no. 2, pp. 210–217, Oct. 2018.
• Md F. Anjum, S. Dasgupta, R. Mudumbai, A. Singh, J. F. Cavanagh, N. S. Narayanan, “Linear predictive coding distinguishes spectral EEG features of parkinson’s disease,” Parkinsonism and amp; Related Disorders, vol. 79, pp. 79–85, Oct. 2020.
• H. W. Loh, C. P. Ooi, E. Palmer, P. D. Barua, S. Dogan, T. Tuncer, M. Baygin, U. R. Acharya, “GaborPDNet: Gabor Transformation and Deep Neural Network for parkinson’s disease detection using EEG signals,” Electronics, vol. 10, no. 14, p. 1740, Jul. 2021.
• S. B. Lee, Y. J. Kim, S. Hwang, H. Son, S. K. Lee, K. I. Park, Y. G. Kim, “Predicting parkinson’s disease using gradient boosting decision tree models with Electroencephalography signals,” Parkinsonism and amp; Related Disorders, vol. 95, pp. 77–85, Feb. 2022.
• I. Suuronen, A. Airola, T. Pahikkala, M. Murtojarvi, V. Kaasinen, H. Railo, “Budget-based classification of parkinson’s disease from resting state EEG,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 8, pp. 3740–3747, Aug. 2023.
• M. F. Karakaş, F. Latifoğlu, “Distinguishing parkinson’s disease with GLCM features from the Hankelization of EEG Signals,” Diagnostics, vol. 13, no. 10, p. 1769, May 2023.
• F. Onay, B. Karaçalı, “Accelerometer-based timing analysis for parkinson’s disease classification,” 2023 31st Signal Processing and Communications Applications Conference (SIU), Jul. 2023.
• B. O. Olcay, F. Onay, G. Akın Öztürk, A. Öniz, M. Özgören, T. Hummel, Ç. Güdücü “Using chemosensory-induced EEG signals to identify patients with de Novo Parkinson’s disease,” Biomedical Signal Processing and Control, vol. 87, p. 105438, Jan. 2024.
• S. L. Oh, Y. Hagiwara, U. Raghavendra, R. Yuvaraj, N. Arunkumar, M. Murugappan, U. Rajendra Acharya, “A deep learning approach for parkinson’s disease diagnosis from EEG signals,” Neural Computing and Applications, vol. 32, no. 15, pp. 10927–10933, 2018.
• L. Qiu, J. Li, J. Pan, “Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional Neural Networks,” Frontiers in Neuroscience, vol. 16, 2022.
• A. M. Maitin, J. P. Romero Munoz, A. 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, 2022.
• “Narayanan lab,” Datasets | Narayanan Lab,https://narayanan.lab.uiowa.edu/article/datasets (accessed Sep. 18, 2023).
• S. Krishnan, Y. Athavale, “Trends in biomedical signal feature extraction,” Biomedical Signal Processing and Control, vol. 43, pp. 41–63, 2018.
• “Power_spectral_density,”Wikipedia,https://en.wikipedia.org/wiki/Spectral_density#Power_spectral_density (accessed Sep. 18, 2023).
• J. R. King, J. D. Sitt, F. Faugeras, L. Cohen, L. Naccache, L. Cohen, L. Naccache, S. Dehaene, “Information sharing in the brain indexes consciousness in noncommunicative patients,” Current Biology, vol. 23, no. 19, pp. 1914–1919, 2013.
• J. D. Sitt, J. R. King, I. E. Karoui, B. Rohaut, F. Faugeras, A. Gramfort, L. Cohen, M. Sigman, S. Dehaene, L. Naccache, “Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state,” Brain, vol. 137, no. 8, pp. 2258–2270, 2014.
• A. Phinyomark, P. Phukpattaranont, C. Limsakul, “Feature reduction and selection for EMG Signal Classification,” Expert Systems with Applications, vol. 39, no. 8, pp. 7420–7431, 2012.
• H. Helakari, J. Kananen, N. Huotari, L. Raitamaa, T. Tuovinen, V. Borchardt, A. Rasila, V. Raatikainen, T. Starck, T. Hautaniemi, T. Myllyla, O. Tervonen, S. Rytky, T. Keinanen, V. Korhonen, V. Kiviniemi, H. Ansakorpi, “Spectral entropy indicates electrophysiological and hemodynamic changes in drug-resistant epilepsy a multimodal MREG study,” NeuroImage: Clinical, vol. 22, p. 101763, 2019.
• A. Vakkuri, A. Yli Hankala, P. Talja, S. Mustola, H. Tolvanen-Laakso, T. Sampson, H. Viertiö-Oja, “Time‐frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia,” Acta Anaesthesiologica Scandinavica, vol. 48, no. 2, pp. 145–153, Jan. 2004.
• “Kolmogorov_complexity,”Wikipedia,https://en.wikipedia.org/wiki/Kolmogorov_complexity (accessed Sep. 18, 2023).
• A. Natekin, A. Knoll, “Gradient Boosting Machines, a tutorial,” Frontiers in Neurorobotics, vol. 7, 2013.
• “K-nearest neighbors algorithm,” Wikipedia,https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm (accessed Sep. 18, 2023).
• “Naive Bayes Classifier,” Wikipedia, https://tr.wikipedia.org/wiki/Naive_Bayes_s%C4%B1n%C4%B1fland%C4%B1r%C4%B1c%C4%B1s%C4%B1 (accessed Sep. 18, 2023).
• V. N. Vapnik, The Nature of Statistical Learning Theory. Cham: Springer International Publishing.
• C. J. C. Burges, “Data Mining and Knowledge Discovery,” vol. 2, no. 2, pp. 121–167, 1998.
• W. Menard, Scott. Logistic regression: From introductory to advanced concepts and applications. Sage, 2010.
• L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, A. Gulin, "CatBoost: unbiased boosting with categorical features." Advances in neural information processing systems 31, 2018.
• V. Morde, “XGBoost algorithm: Long may she reign!,” Medium, https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d (accessed May 26, 2024).
• E. Alpaydin. Introduction to machine learning. MIT press, 2020.
• N. Baki, N. Gürsel Özmen, “An early diagnosis approach for parkinson patients without cognitive disorder from EEG Data,” 2022 Medical Technologies Congress (TIPTEKNO), Antalya, Turkey, 2022, pp. 1-4, doi: 10.1109/TIPTEKNO56568.2022.996020. Oct. 2022.
• C. Gomez, K. T. E. Olde Dubbelink, C. J. Stam, D. Abasolo, H. W. Berendse, R. Hornero, “Complexity analysis of resting-state MEG activity in early-stage parkinson’s disease patients,” Annals of Biomedical Engineering, vol. 39, no. 12, pp. 2935–2944, 2011.
• G. S. Yi, J. Wang, B. Deng, X. L. Wei, "Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease," Cognitive Neurodynamics, vol. 11, pp. 147-160, 2017.
• L. Pezard, R. Jech, E. Ruzicka, "Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson’s disease," Clinical Neurophysiology, vol. 165, no. 1, pp. 38–45, 2001, PMID: 11137659.
• J. Bosboom, D. Stoffers, C. Stam, B. Dijk, J. Verbunt, H. Berendse, E.Ch. Wolters, "Resting state oscillatory brain dynamics in Parkinson’s disease: An MEG study," Clinical Neurophysiology, vol. 117, no. 11, pp. 2521–2531, 2006.
• Q. Wang, L. Meng, J. Pang, X. Zhu, D. Ming, "Characterization of EEG data revealing relationships with cognitive and motor symptoms in Parkinson's disease: A systematic review," Frontiers in Aging Neuroscience, cilt 12, sayı 587396, 2020.