EEG Signal Analysis for Automated Sleep State Detection

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Solution Overview

Problem

Current methods for analyzing sleep states via EEG signals, such as the Rechtschaffen-Kales method, are unreliable, time-consuming, and lack temporal and spatial resolution due to low power frequency limitations, leading to poor inter-user agreement and inadequate detection of high-frequency shifts.

Innovation Solution

The method involves normalizing EEG data to increase the dynamic range of information, allowing for automatic determination of sleep states, assessment of sleep quality, and identification of pathological conditions by extracting low power frequency information and classifying sleep stages using frequency weighting and clustering techniques.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual scoring methods (Rechtschaffen-Kales) are used, then reliability can be maintained, but time consumption increases and temporal resolution deteriorates

Engineering Contradiction:

ImprovereliabilityVSAvoidtime consuming

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual scoring mechanisms with automated computer-based analysis systems that process EEG signals through algorithmic classification, eliminating the time-consuming manual review process while maintaining scoring reliability through standardized automated criteria

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms continuous EEG signal parameters into discrete sleep stage classifications through automated detection algorithms, changing the analysis approach from manual interpretation to automated parameter-based classification that reduces time loss

Inventive Principle:

Principle #35Parameter changes

2Loss of time

If automated signal analysis is used, then time consumption decreases, but measurement precision deteriorates due to low power frequency limitations

Engineering Contradiction:

Improvetime consumingVSAvoidmeasurement precision

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The patent introduces frequency weighting as an additional dimension to the analysis, transforming the power spectrum through frequency-dependent weighting functions that amplify low-power high-frequency components, thereby enhancing measurement precision without increasing time consumption

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Solution Approach 2:

The system applies frequency weighting transformations to change the spectral parameters of the EEG signal, converting low-power frequencies into detectable signals through mathematical transformation while maintaining automated analysis speed

Inventive Principle:

Principle #35Parameter changes

3Productivity

If conventional automated analysis is used, then productivity increases, but measurement precision deteriorates due to inability to detect high frequency shifts

Engineering Contradiction:

ImproveproductivityVSAvoiddetection of high frequency shifts

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent applies frequency weighting functions that create resonant amplification of specific frequency bands, enabling the detection of high-frequency shifts that would otherwise be lost in the noise, thereby improving measurement precision while maintaining high productivity

Inventive Principle:

Principle #18Mechanical vibration

Solution Approach 2:

Frequency weighting acts as an intermediary transformation layer between the raw EEG signal and the analysis algorithm, amplifying weak high-frequency components to detectable levels without requiring changes to the underlying signal or analysis methodology

Inventive Principle:

Principle #24Intermediary (Mediator)

4Measurement precision

If multiple channels are used, then measurement precision improves, but device complexity increases

Engineering Contradiction:

Improvemeasurement precisionVSAvoiddevice complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent makes the frequency weighting function universal by applying the same transformation methodology across all EEG channels, allowing single-channel or multi-channel systems to benefit from enhanced precision through the same algorithmic approach without proportionally increasing system complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11690557B2Automated detection of sleep and waking states

Publication Date: 2023.07.04 SALK INST FOR BIOLOGICAL STUDIES

AI summary

Determining low power frequency range information from spectral data. Raw signal data can be adjusted to increase dynamic range for power within low power frequency ranges as compared to higher-power frequency ranges to determine adjusted source data valuable for acquiring low power frequency range information. Low power frequency range information can be used in the analysis of a variety of raw signal data. For example, low power frequency range information within electroencephalography data for a subject from a period of sleep can be used to determine sleep states. Similarly, automated full-frequency spectral electroencephalography signal analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, and determining the effect of medication on sleep states.