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Phase-amplitude coupling analysis for seizure evolvement using Hilbert Huang Transform.

Hanyue Zhou, Ying Li, Yue-Loong Hsin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
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    A new Hilbert Huang Transform (HHT) method reveals clearer phase-amplitude coupling (PAC) patterns in brain signals. This approach overcomes limitations of traditional Fourier methods for analyzing nonstationary biological data, offering improved insights into epilepsy.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Biomedical Engineering

    Background:

    • Phase-amplitude coupling (PAC) is crucial for brain information processing and cognition.
    • Conventional PAC analysis uses Fourier-based filters, assuming signal stationarity and linearity.
    • Biological signals are often nonstationary and nonlinear, leading to inaccuracies with traditional methods.

    Purpose of the Study:

    • To introduce a novel Hilbert Huang Transform (HHT)-based method for PAC analysis.
    • To address the limitations of Fourier-based methods in analyzing nonstationary and nonlinear biological signals.
    • To reveal regular PAC patterns during seizure evolution in epilepsy patients.

    Main Methods:

    • Application of the Hilbert Huang Transform (HHT) for signal decomposition.
    • Analysis of intracranial EEG signals from epilepsy patients.
    • Visualization of PAC comodulograms in the Intrinsic Mode Function (IMF) domain.

    Main Results:

    • The HHT-based method reveals clear and regular PAC patterns.
    • PAC patterns are observed across different seizure stages during seizure evolution.
    • The proposed method demonstrates superior performance compared to conventional Fourier-based techniques.

    Conclusions:

    • The HHT-based PAC analysis method offers a more accurate and insightful approach for biological signals.
    • This novel method can identify distinct PAC patterns relevant to neurological conditions like epilepsy.
    • The IMF domain provides a more suitable framework for understanding PAC in complex brain dynamics.