Computer methods and programs in biomedicine · 2025
Measuring phase-amplitude coupling using dispersion fuzzy mutual information.
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Zhang Hao, Bian Zhijie, Guo Xiaonan, Li Xiaoli, Yin Shimin, Cui Dong
Аннотация
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.
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.