EEG Bispectrum Analysis for Sleep State Classification

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Solution Overview

Problem

Current automatic sleep scoring systems for diagnosing obstructive sleep apnea hypopnea syndrome (OSAHS) face challenges such as reliance on visual features, high subjectivity, and inability to accurately distinguish wake states from NREM or REM sleep, especially in conditions with frequent EEG arousals and artifacts, leading to variability and inefficiency in diagnosis.

Innovation Solution

A method and apparatus that electronically processes EEG signals to generate higher-order spectra, specifically bispectra, to classify segments into macro-sleep states like Wake, NREM, and REM sleep, using predetermined criteria and threshold values, and provides a sleepiness index for objective monitoring.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automatic sleep scoring systems use visual features and manual interpretation, then diagnostic capability is maintained, but subjectivity increases and consistency decreases

Engineering Contradiction:

Improvesleep state classification accuracyVSAvoiddiagnosis consistency

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent replaces manual visual interpretation (mechanical human analysis) with automated signal processing algorithms. Specifically, it transforms EEG signals into higher-order spectral representations (bispectrum, trispectrum) and uses automated pattern recognition to classify sleep states, eliminating human subjectivity while maintaining diagnostic accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the parameter representation of EEG signals from traditional time-domain or power spectrum analysis to higher-order spectral parameters (bispectrum, trispectrum). This transformation reveals nonlinear relationships and phase coupling information that are invisible in conventional analysis, enabling more accurate and consistent sleep state classification.

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If traditional EEG analysis methods are used, then computational simplicity is maintained, but ability to distinguish wake states from NREM or REM sleep deteriorates

Engineering Contradiction:

Improvesignal processing complexityVSAvoidsleep state differentiation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent transitions from two-dimensional power spectrum analysis to three-dimensional higher-order spectral analysis (bispectrum and trispectrum). This additional dimension captures phase relationships and nonlinear interactions between frequency components, providing richer features for distinguishing between wake, NREM, and REM states that are invisible in conventional analysis.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Measurement precision

If manual sleep scoring is performed, then diagnostic thoroughness is maintained, but time consumption increases and productivity decreases

Engineering Contradiction:

Improvesleep disorder diagnosis accuracyVSAvoiddiagnosis throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent implements self-service automation where the system automatically performs sleep state classification and generates diagnostic reports without requiring manual intervention. The automated algorithm processes EEG signals, classifies sleep stages, identifies sleep disorders, and produces results that can be reviewed by clinicians, dramatically increasing diagnosis throughput while maintaining accuracy.

Inventive Principle:

Principle #25Self-service

4Use of energy by moving object

If conventional power spectrum analysis is used, then computational efficiency is maintained, but detection of nonlinear EEG characteristics deteriorates

Engineering Contradiction:

Improvecomputational energy consumptionVSAvoidnonlinear signal information

Core Design Contradiction:

Use of energy by moving objectVSLoss of information

Solution Approach 1:

The patent changes the analytical parameters from linear power spectrum to nonlinear higher-order spectra (bispectrum, trispectrum). These parameters capture phase coupling, frequency mixing, and nonlinear interactions in EEG signals that are completely lost in conventional power spectrum analysis, providing insight into the dynamic reorganization of brain activity during sleep transitions.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10575751B2Method and apparatus for determining sleep states

Publication Date: 2020.03.03 THE UNIVERSITY OF QUEENSLAND

AI summary

An apparatus is provided for detecting Macro Sleep Architecture states of a subject such as WAKE, NREM and REM sleep from a subject's EEG. The apparatus includes an EEG digital signal assembly of modules arranged to convert analogue EEG signals into digital EEG signals. A bispectrum assembly is responsive to the EEG digital signal assembly and converts the digital EEG signals into signals representing corresponding bispectrum values. A bispectrum time series assembly, in electrical communication with an output side of the bispectrum assembly, generates at least one bispectrum time series for a predetermined frequency. A macro-sleep architecture (MSA) assembly is responsive to the bispectrum time series assembly and is arranged to produce classification signals indicating classification of segments of the EEG signals into macro-sleep states of the subject.