Stochastic Neural Network Sleep Parameter Uncertainty Estimation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current automatic sleep scoring systems lack the ability to accurately estimate uncertainty in overnight sleep parameters, leading to inconsistencies and inefficiencies in diagnosing sleep disorders like sleep apnea, due to low inter-rater agreement among human scorers and limitations in long-term modeling.

Innovation Solution

A stochastic neural network-based system that receives physiological data during sleep studies, generates multiple hypnograms, calculates robust estimates of sleep parameters, and determines uncertainty, allowing for repeated studies or adjustments in sensor configurations based on threshold values to improve data quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If conventional automatic sleep scoring algorithms are used, then automation is achieved, but uncertainty estimation capability is lost

Engineering Contradiction:

Improveautomation of sleep scoringVSAvoiduncertainty estimation capability

Core Design Contradiction:

Extent of automationVSReliability

Solution Approach 1:

The patent introduces an intermediary component - a Bayesian neural network layer - that sits between the automated sleep scoring process and the final output. This intermediary enables uncertainty estimation by modeling the probability distribution of predictions rather than providing deterministic results, thus maintaining automation while adding reliability assessment capability

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback by using the estimated uncertainty values to guide further processing decisions. When uncertainty exceeds thresholds, the system triggers additional processing such as requesting alternative measurements or flagging for expert review, creating a closed-loop system that improves reliability based on uncertainty feedback

Inventive Principle:

Principle #23Feedback

2Measurement precision

If human scorers perform sleep staging, then diagnostic accuracy is improved, but labor intensity increases

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidlabor efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent segments the sleep scoring task into two parts: routine scoring performed by the automated Bayesian neural network system, and uncertain cases flagged for expert human review. This segmentation allows most cases to be processed efficiently by automation while maintaining high diagnostic accuracy for ambiguous cases through human expertise

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The automated system performs self-assessment of its own confidence through uncertainty estimation. Cases where the system is highly confident are processed autonomously without human intervention, while only uncertain cases require human review, allowing the system to serve itself for the majority of straightforward cases

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If surrogate measurements like PPG or actigraphy are used, then longitudinal and ambulatory studies are enabled, but scoring accuracy deteriorates

Engineering Contradiction:

Improvecapability for longitudinal and ambulatory studiesVSAvoidsleep staging accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent adapts the Bayesian neural network model to work with different measurement parameters and modalities. The system can be configured to process surrogate measurements like PPG or actigraphy by adjusting the input parameters and training data accordingly, enabling accurate sleep staging from alternative measurement types that support ambulatory and longitudinal studies

Inventive Principle:

Principle #35Parameter changes

4Productivity

If existing automated algorithms are used, then processing speed is improved, but long-term uncertainty modeling is lost

Engineering Contradiction:

Improveprocessing speedVSAvoidlong-term uncertainty modeling

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements dynamic uncertainty modeling that adapts to the specific characteristics of each sleep recording. The Bayesian neural network dynamically adjusts its uncertainty estimates based on the input data patterns, allowing accurate long-term uncertainty modeling while maintaining efficient processing speeds through optimized computational approaches

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20240206805A1System for estimating uncertainty of overnight sleep parameters through a stochastic neural network and method of operation thereof

Publication Date: 2024.06.27 KONINKLIJKE PHILIPS NV

AI summary

A sleep monitoring system (100, 500), including: a sensor (114) to sense physiological changes of a subject during a sleep study period and form corresponding sleep study information; a stochastic neural network (129, 329) to: receive the sleep study information; and derive a plurality of sleep sample evaluations each different from each other and based on a same sampling of the received sleep study information; a controller (120, 190, 194) to: receive the plurality of sleep sample evaluations and calculate a robust estimate of one or more sleep parameters thereon; calculate an estimate of uncertainty for at least one of the one or more sleep parameters; determine whether the estimate of uncertainty for the at least one of the one or more sleep parameters is greater than a threshold value; and render the estimate of uncertainty on a rendering device (192) of the system based upon the determination.