Holo-Hilbert Spectral Analysis for Non-Stationary Brain Signals
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Solution Overview
Problem
Conventional methods for analyzing brain electrical activities, such as EEG and MEG, face challenges due to the non-stationary and non-linear nature of these signals, which are not effectively addressed by existing signal processing techniques, leading to difficulties in visualization and diagnosis of neurological disorders.
Innovation Solution
The implementation of Holo-Hilbert spectral analysis (HOSA) based methods and systems that generate novel probability distribution functions and multi-scale entropy, providing visual outputs for EEG, MEG, and ECoG signals to facilitate better analysis and diagnosis of neurological disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional signal processing methods (Fourier transformation) are used to analyze brain electrical activities, then the analysis process is simple and straightforward, but the methods fail to effectively address the non-stationary and non-linear nature of EEG, MEG, and ECoG signals, leading to inability to provide meaningful visualization results
Solution Approach 1:
The patent transforms the analysis approach by changing the mathematical parameters and methods used. Instead of conventional Fourier transformation, it employs Hilbert-Huang transformation and HOSA techniques that are specifically suited for non-stationary and non-linear signals. This parameter change enables effective extraction of instantaneous frequency, amplitude, and phase information from brain electrical activities while maintaining analytical capability.
Solution Approach 2:
The patent substitutes conventional signal processing mechanisms with advanced mathematical transformation methods. It replaces Fourier-based linear analysis with Hilbert-Huang-based non-linear analysis, enabling the system to handle the complex, dynamic nature of brain signals. This substitution introduces new analytical capabilities while preserving the core function of signal visualization and interpretation.
2Measurement precision
If Holo-Hilbert spectral analysis (HOSA) based methods are implemented to effectively analyze non-stationary and non-linear brain signals, then the visualization and diagnostic capability is significantly improved, but the complexity of the analysis system and processing methodology increases
Solution Approach 1:
The patent segments the complex analysis process into distinct functional modules: signal acquisition module, Hilbert-Huang transformation module, HOSA analysis module, and visualization module. Each module performs a specific function, making the overall complex system more manageable and interpretable. This segmentation allows the system to handle non-stationary and non-linear signals effectively while organizing the complexity into manageable components.
Solution Approach 2:
The patent introduces intermediate processing steps and mathematical transformations as mediators between the raw brain signals and the final visualization. The Hilbert-Huang transformation serves as an intermediary that decomposes complex signals into intrinsic mode functions, and HOSA acts as another intermediary that synthesizes these into meaningful spectral representations. These intermediaries bridge the gap between complex input signals and interpretable visual outputs.
3Ease of operation
If conventional probability distribution function is used for signal analysis, then the method is simple to apply, but it requires signals to be stationary and with large amplitude variations, which limits its applicability to non-stationary brain electrical activities
Solution Approach 1:
The patent introduces dynamic analysis capabilities through Hilbert-Huang transformation and HOSA, which can adapt to changing signal characteristics in real-time. Unlike conventional probability distribution functions that assume stationarity, these methods can track instantaneous frequency, amplitude, and phase variations dynamically. This dynamic approach enables the system to analyze non-stationary brain electrical activities effectively while maintaining ease of application through automated processing.
Data Source
AI summary
The present disclosure provides a non-transitory computer program product embodied in a computer-readable medium and, when executed by one or more analysis modules, providing a visual output for presenting physiological signals of a cardiovascular system. The non-transitory computer program product comprises a first axis representing, subsets of intrinsic mode functions (IMF); a second axis representing a function of signal strength in a time interval; and a plurality of visual elements, each of the visual elements being defined by the first axis and the second axis, and each of the visual elements comprising a plurality of analyzed data units collected over the time interval. Wherein each of the analyzed data units comprises a first coordinate, a second coordinate, and a probability density value generated from an intrinsic probability density function of one of the subsets of IMFs.