Measuring phase-amplitude coupling using dispersion fuzzy mutual information.
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| Názov: | Measuring phase-amplitude coupling using dispersion fuzzy mutual information. |
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| Autori: | Zhang H; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China; School of Information Science and Engineering, Yan Shan University, Qinhuangdao 066004, China., Bian Z; Department of Neurology, Beijing Friendship Hospital, Beijing 100050 China., Guo X; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China; School of Information Science and Engineering, Yan Shan University, Qinhuangdao 066004, China., Li X; Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou 510335, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China., Yin S; Department of Neurology, The Rocket Force Characteristic Medical Center of Chinese People's Liberation Army, Beijing 100088, China., Cui D; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China; School of Information Science and Engineering, Yan Shan University, Qinhuangdao 066004, China. Electronic address: cuidong@ysu.edu.cn. |
| Zdroj: | Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2025 Dec; Vol. 272, pp. 109075. Date of Electronic Publication: 2025 Sep 17. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Scientific Publishers Original Publication: Amsterdam : Elsevier Science Publishers, c1984- |
| Výrazy zo slovníka MeSH: | Fuzzy Logic* , Electroencephalography*, Humans ; Cognitive Dysfunction/physiopathology ; Cognitive Dysfunction/diagnosis ; Algorithms ; Alzheimer Disease/diagnosis ; Alzheimer Disease/physiopathology ; Entropy ; Signal Processing, Computer-Assisted ; Computer Simulation |
| Abstrakt: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Background: Mild Cognitive Impairment (MCI) is a preliminary stage of Alzheimer's disease (AD), and early diagnosis of MCI electroencephalography (EEG) signals using the Phase-Amplitude Coupling (PAC) phenomenon in neural oscillations as an EEG marker has become a promising technique. Nonetheless, the PAC estimators, which are frequently employed in clinical practice, exhibit considerable limitations with regard to their application conditions. In order to explore a PAC estimator with strong applicability, the Dispersion Fuzzy Mutual Information (DFMI) method is proposed. Methods: The DFMI method employs the symbolization principle of dispersion entropy and mutual information theory to process time series. This approach addresses the challenges posed by ambiguous quantitative fluctuations in the number of patterns in fuzzy entropy and upgrades the single-channel fuzzy entropy algorithm to a dual-channel DFMI algorithm. Subsequently, through simulation analysis, it was compared with the commonly used PAC estimator in clinical practice in terms of coupling strength sensitivity, data length dependency, noise resistance, coupling frequency band sensitivity, and artifact resistance. Results: The simulation results indicate that the DFMI method can effectively obtain PAC strength, is less dependent on data length, produces stable calculation results, and is less affected by pseudo-trace signals. The MCI-EEG data results demonstrated that MCI patients significantly enhanced whole-brain theta-gamma coupling activity, while alpha-gamma coupling activity shifted to the low-frequency band. Conclusion: The DFMI can be utilized as a PAC estimator to assess PAC phenomena in neural signals, and the coupling of neural oscillations in the MCI brain may manifest as coupling band attenuation. (Copyright © 2025. Published by Elsevier B.V.) |
| Contributed Indexing: | Keywords: Dispersion fuzzy mutual information; Electroencephalography; Mild cognitive impairment; Phase-Amplitude coupling |
| Entry Date(s): | Date Created: 20250925 Date Completed: 20251013 Latest Revision: 20251013 |
| Update Code: | 20251013 |
| DOI: | 10.1016/j.cmpb.2025.109075 |
| PMID: | 40997716 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Background: Mild Cognitive Impairment (MCI) is a preliminary stage of Alzheimer's disease (AD), and early diagnosis of MCI electroencephalography (EEG) signals using the Phase-Amplitude Coupling (PAC) phenomenon in neural oscillations as an EEG marker has become a promising technique. Nonetheless, the PAC estimators, which are frequently employed in clinical practice, exhibit considerable limitations with regard to their application conditions. In order to explore a PAC estimator with strong applicability, the Dispersion Fuzzy Mutual Information (DFMI) method is proposed.<br />Methods: The DFMI method employs the symbolization principle of dispersion entropy and mutual information theory to process time series. This approach addresses the challenges posed by ambiguous quantitative fluctuations in the number of patterns in fuzzy entropy and upgrades the single-channel fuzzy entropy algorithm to a dual-channel DFMI algorithm. Subsequently, through simulation analysis, it was compared with the commonly used PAC estimator in clinical practice in terms of coupling strength sensitivity, data length dependency, noise resistance, coupling frequency band sensitivity, and artifact resistance.<br />Results: The simulation results indicate that the DFMI method can effectively obtain PAC strength, is less dependent on data length, produces stable calculation results, and is less affected by pseudo-trace signals. The MCI-EEG data results demonstrated that MCI patients significantly enhanced whole-brain theta-gamma coupling activity, while alpha-gamma coupling activity shifted to the low-frequency band.<br />Conclusion: The DFMI can be utilized as a PAC estimator to assess PAC phenomena in neural signals, and the coupling of neural oscillations in the MCI brain may manifest as coupling band attenuation.<br /> (Copyright © 2025. Published by Elsevier B.V.) |
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| ISSN: | 1872-7565 |
| DOI: | 10.1016/j.cmpb.2025.109075 |