Resting-state electroencephalogram relevance state recognition of Parkinson’s disease based on dynamic weighted symbolic mutual information and <i>k</i>-means clustering_Journal of Biomedical Engineering

Authors：

DING Hao , WU Jinhui , TANG Xudong , YU Jiangnan , CHEN Xuanheng ,  WU Zhanxiong

School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, P. R. China;

Corresponding author：

WU Zhanxiong, Email: wzx@hdu.edu.cn

Keywords：

Parkinson’s disease; Electroencephalogram; Weighted symbolic mutual information; k-means clustering

DOI：

10.7507/1001-5515.202211002

Video：

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At present, the incidence of Parkinson’s disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band (P = 0.034) and State5 of Gamma frequency band (P = 0.010) could be used to differentiate health controls and off-medication Parkinson’s disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson’s disease.

Citation： DING Hao, WU Jinhui, TANG Xudong, YU Jiangnan, CHEN Xuanheng, WU Zhanxiong. Resting-state electroencephalogram relevance state recognition of Parkinson’s disease based on dynamic weighted symbolic mutual information and k-means clustering. Journal of Biomedical Engineering, 2023, 40(1): 20-26. doi: 10.7507/1001-5515.202211002 Copy

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2.	杨一风, 胡颖, 聂生东. 基于磁共振图像的帕金森病计算机辅助诊断研究进展. 中国生物医学工程学报, 2020, 39(5): 603-610.
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4.	Yu Zhu, Min Yuan, Yue Liu, et al. Association between inflammatory bowel diseases and Parkinson’s disease: systematic review and meta-analysis. 中国神经再生研究(英文版), 2022, 17(2): 344-353.
5.	Bočková M, Rektor I. Impairment of brain functions in Parkinson’s disease reflected by alterations in neural connectivity in EEG studies: A viewpoint. Clin Neurophysiol, 2019, 130(2): 239-247.
6.	刘瑾, 杨新新, 项洁. 帕金森病康复治疗及其作用机制研究进展. 中国现代神经疾病杂志, 2017, 17(6): 403-408.
7.	Zhu M, HajiHosseini A, Baumeister T R, et al. Altered EEG alpha and theta oscillations characterize apathy in Parkinson’s disease during incentivized movement. Neuroimage Clin, 2019, 23: 101922.
8.	Miladinović A, Ajčević M, Busan P, et al. EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands. Proced Comput Sci, 2021, 192(2): 2616-2623.
9.	Lainscsek C, Hernandez M, Weyhenmeyer J, et al. Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson’s disease from healthy individuals. Front Neurol, 2013, 4: 200.
10.	Palmer S J, Lee P W, Jane Z, et al. θ, β but not α-band EEG connectivity has implications for dual task performance in Parkinson’s disease. Parkinsonism Relat Disord, 2010, 16(6): 393-397.
11.	Jaramillo-Jimenez A, Suarez-Revelo J X, Ochoa-Gomez J F, et al. Resting-state EEG alpha/theta ratio related to neuropsychological test performance in Parkinson’s disease. Clin Neurophysiol, 2021, 132(3): 756-764.
12.	Jackson N, Cole S R, Voytek B, et al. Characteristics of waveform shape in Parkinson’s disease detected with scalp electroencephalography. eNeuro, 2019, 6(3): ENEURO.0151-19.2019.
13.	Stoffers D, Bosboom J L W, Deijen J B, et al. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s disease without dementia. Brain, 2007, 130(7): 1847-1860.
14.	Soikkeli R, Partanen J, Soininen H, et al. Slowing of EEG in Parkinson’s disease. Electroencephalogr Clin Neurophysiol, 1991, 79(3): 159-165.
15.	Takeuchi M, Iwata M, Osawa M. A study on the background activities of EEG in Parkinson disease. Int Congress Ser, 2005, 1278: 337-340.
16.	Zhang R, Jia J. EEG analysis of Parkinson’s disease using time-frequency analysis and deep learning. Biomed Signal Process Control, 2022, 78: 103883.
17.	Gerard M, Bayot M, Derambure P, et al. EEG-based functional connectivity and executive control in patients with Parkinson’s disease and freezing of gait. Clin Neurophysiol, 2022, 137: 207-215.
18.	Conti M, Bovenzi R, Garasto E, et al. Brain functional connectivity in de novo Parkinson’s disease patients based on clinical EEG. Front Neurol, 2022, 13: 844745.
19.	Imperatori L S, Betta M, Cecchetti L, et al. EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions. Sci Rep, 2019, 9: 8894.
20.	Fu Z, Armin I, Turner J A, et al. Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia. NeuroImage, 2021, 224: 117385.
21.	Bourdillon P, Hermann B, Guénot M, et al. Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study. Sci Rep, 2020, 10(1): 14037.
22.	George J S, Strunk J, Mak-McCully R, et al. 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 Clin, 2013, 3: 261-270.
23.	King J, Sitt J D, Faugeras F, et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr Biol, 2012, 23(9): 1914-1919.
24.	Díez-Cirarda M, Strafella A P, Kim J, et al. Dynamic functional connectivity in Parkinson’s disease patients with mild cognitive impairment and normal cognition. Neuroimage Clin, 2017, 17: 847-855.
25.	Fu Z, Tu Y, Di X, et al. Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamicfunctional connectivity: an application to schizophrenia. NeuroImage, 2018, 180(Part B): 619-631.
26.	Ikotun A M, Ezugwu A E, Abualigah L, et al. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Inf Sci, 2023, 622: 178-210.

1. 周超宁, 陈军, 谷有全, 等. 帕金森病患者血脂血尿酸水平与疾病进展的相关性研究. 中国实用神经疾病杂志, 2020, 23(21): 1847-1851.
2. 杨一风, 胡颖, 聂生东. 基于磁共振图像的帕金森病计算机辅助诊断研究进展. 中国生物医学工程学报, 2020, 39(5): 603-610.
3. Lee S, Hussein R, Ward R, et al. A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson’s disease. J Neurosci Meth, 2021, 361(2021): 109282.
4. Yu Zhu, Min Yuan, Yue Liu, et al. Association between inflammatory bowel diseases and Parkinson’s disease: systematic review and meta-analysis. 中国神经再生研究(英文版), 2022, 17(2): 344-353.
5. Bočková M, Rektor I. Impairment of brain functions in Parkinson’s disease reflected by alterations in neural connectivity in EEG studies: A viewpoint. Clin Neurophysiol, 2019, 130(2): 239-247.
6. 刘瑾, 杨新新, 项洁. 帕金森病康复治疗及其作用机制研究进展. 中国现代神经疾病杂志, 2017, 17(6): 403-408.
7. Zhu M, HajiHosseini A, Baumeister T R, et al. Altered EEG alpha and theta oscillations characterize apathy in Parkinson’s disease during incentivized movement. Neuroimage Clin, 2019, 23: 101922.
8. Miladinović A, Ajčević M, Busan P, et al. EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands. Proced Comput Sci, 2021, 192(2): 2616-2623.
9. Lainscsek C, Hernandez M, Weyhenmeyer J, et al. Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson’s disease from healthy individuals. Front Neurol, 2013, 4: 200.
10. Palmer S J, Lee P W, Jane Z, et al. θ, β but not α-band EEG connectivity has implications for dual task performance in Parkinson’s disease. Parkinsonism Relat Disord, 2010, 16(6): 393-397.
11. Jaramillo-Jimenez A, Suarez-Revelo J X, Ochoa-Gomez J F, et al. Resting-state EEG alpha/theta ratio related to neuropsychological test performance in Parkinson’s disease. Clin Neurophysiol, 2021, 132(3): 756-764.
12. Jackson N, Cole S R, Voytek B, et al. Characteristics of waveform shape in Parkinson’s disease detected with scalp electroencephalography. eNeuro, 2019, 6(3): ENEURO.0151-19.2019.
13. Stoffers D, Bosboom J L W, Deijen J B, et al. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s disease without dementia. Brain, 2007, 130(7): 1847-1860.
14. Soikkeli R, Partanen J, Soininen H, et al. Slowing of EEG in Parkinson’s disease. Electroencephalogr Clin Neurophysiol, 1991, 79(3): 159-165.
15. Takeuchi M, Iwata M, Osawa M. A study on the background activities of EEG in Parkinson disease. Int Congress Ser, 2005, 1278: 337-340.
16. Zhang R, Jia J. EEG analysis of Parkinson’s disease using time-frequency analysis and deep learning. Biomed Signal Process Control, 2022, 78: 103883.
17. Gerard M, Bayot M, Derambure P, et al. EEG-based functional connectivity and executive control in patients with Parkinson’s disease and freezing of gait. Clin Neurophysiol, 2022, 137: 207-215.
18. Conti M, Bovenzi R, Garasto E, et al. Brain functional connectivity in de novo Parkinson’s disease patients based on clinical EEG. Front Neurol, 2022, 13: 844745.
19. Imperatori L S, Betta M, Cecchetti L, et al. EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions. Sci Rep, 2019, 9: 8894.
20. Fu Z, Armin I, Turner J A, et al. Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia. NeuroImage, 2021, 224: 117385.
21. Bourdillon P, Hermann B, Guénot M, et al. Brain-scale cortico-cortical functional connectivity in the delta-theta band is a robust signature of conscious states: an intracranial and scalp EEG study. Sci Rep, 2020, 10(1): 14037.
22. George J S, Strunk J, Mak-McCully R, et al. 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 Clin, 2013, 3: 261-270.
23. King J, Sitt J D, Faugeras F, et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr Biol, 2012, 23(9): 1914-1919.
24. Díez-Cirarda M, Strafella A P, Kim J, et al. Dynamic functional connectivity in Parkinson’s disease patients with mild cognitive impairment and normal cognition. Neuroimage Clin, 2017, 17: 847-855.
25. Fu Z, Tu Y, Di X, et al. Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamicfunctional connectivity: an application to schizophrenia. NeuroImage, 2018, 180(Part B): 619-631.
26. Ikotun A M, Ezugwu A E, Abualigah L, et al. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Inf Sci, 2023, 622: 178-210.

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Resting-state electroencephalogram relevance state recognition of Parkinson’s disease based on dynamic weighted symbolic mutual information and k-means clustering

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