Single-Channel EOG Sleep Staging via Frequency Analysis

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Solution Overview

Problem

Current methods for analyzing sleep patterns using EEG data are invasive, require multiple channels, and struggle to accurately differentiate sleep stages, especially REM and deep sleep, due to the subjective nature of scoring and the difficulty in detecting high-frequency signals through the skull.

Innovation Solution

A novel analysis method using single-channel EEG or electrooculography (EOG) that employs algorithms to detect frequency waves and classify sleep stages, including REM and deep sleep, without the need for invasive techniques, allowing for precise identification of sleep and wake patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multiple channels of EEG data are used to analyze sleep patterns, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improvesleep stage differentiation accuracyVSAvoidnumber of electrodes and channels

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The invention extracts and utilizes electrooculography (EOG) signals from the scalp, which contain high-frequency information about eye movements and brain activity. By focusing on this specific extracted signal rather than requiring multiple EEG channels, the method achieves accurate sleep stage differentiation (particularly for REM detection) while reducing the number of electrodes needed from multiple EEG channels to a minimal EOG configuration

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The invention replaces the traditional mechanical/electrical EEG measurement system with an optical-based EOG measurement system. This substitution allows for non-invasive detection of sleep stages through optical detection of eye movements and associated electrical signals on the scalp surface, eliminating the need for invasive skull penetration and multiple electrode placements

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If invasive techniques are used to detect high-frequency brain signals, then measurement precision is improved, but object-affected harmful factors increase

Engineering Contradiction:

Improvehigh-frequency signal detectionVSAvoidinvasiveness and patient discomfort

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The invention uses the scalp and electrooculography signals as an intermediary to access high-frequency brain activity information. Instead of directly measuring brain signals through invasive skull penetration, the method detects EOG signals on the scalp surface that contain correlated high-frequency information, providing an indirect but non-invasive pathway to the desired measurement

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The invention replaces invasive electrical EEG measurement through skull penetration with non-invasive optical EOG measurement on the scalp surface. This substitution maintains the ability to detect high-frequency signals related to brain activity and eye movements while completely eliminating the harmful effects of invasive procedures

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If manual scoring of sleep data is performed, then measurement precision is improved, but productivity decreases

Engineering Contradiction:

Improvesubjective scoring accuracyVSAvoiddata analysis speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The invention enables automatic sleep stage classification by algorithms that process EOG signal characteristics independently. The system extracts relevant features from the EOG data and autonomously determines sleep stages without requiring manual reviewer intervention, making the analysis process self-sufficient and dramatically increasing productivity while maintaining consistent objective criteria

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The invention changes the analysis parameters from manual visual inspection criteria to automated signal processing parameters based on EOG frequency content and amplitude characteristics. This parameter transformation enables algorithmic classification that is both rapid and objective, resolving the contradiction between manual precision and automated speed

Inventive Principle:

Principle #35Parameter changes

4Device complexity

If single-channel EEG or EOG is used, then device complexity is reduced, but measurement precision worsens

Engineering Contradiction:

Improvenumber of channelsVSAvoidsleep stage classification accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The invention applies local quality by focusing measurement resources on the specific EOG signal characteristics that are most informative for sleep stage detection. Rather than distributing measurement across multiple EEG channels, the method concentrates on extracting and analyzing the particular frequency and amplitude properties of EOG signals from the scalp, achieving high precision through targeted local analysis

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11696724B2Methods of identifying sleep and waking patterns and uses

Publication Date: 2023.07.11 NEUROVIGIL INC

AI summary

Traditional analysis of sleep patterns requires several channel of data. This analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, determining the effect of medication on sleep states and identifying biomarkers, and drug dosages or reactions.