Sleep Onset Latency Detection Using Eye Blink and EEG Signals
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Solution Overview
Problem
Current sleep monitoring systems fail to accurately determine sleep onset latency due to limitations in detecting sleep intention and onset, leading to inconsistent diagnostic accuracy for sleep disorders and potential misdiagnosis.
Innovation Solution
A system comprising sensors and hardware processors that detect brain activity through EEG and other signals to determine sleep stages, intention, and onset latency, using eye blinks and brain activity power thresholds to differentiate between wakefulness and sleep, with sensory stimulators to enhance sleep quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sleep monitoring systems use movement and light sensors to detect sleep onset, then the system complexity remains low, but the measurement precision of sleep onset latency deteriorates
Solution Approach 1:
The patent introduces an intermediary computational layer that processes sensor data through multiple algorithms (movement analysis, light exposure analysis, eye blink detection, brain activity analysis) to mediate between raw sensor inputs and the final sleep onset latency determination, thereby improving measurement precision without requiring a proportionate increase in physical device complexity
Solution Approach 2:
The system employs multi-functional sensors that serve multiple purposes: movement sensors detect both gross body movement and subtle eye blinks, while brain activity sensors simultaneously monitor cortical patterns and sleep stage transitions. This multi-functionality improves measurement accuracy without adding separate dedicated sensors for each function
2Measurement precision
If the system uses multiple detection methods including eye blinks and brain activity to improve sleep intention detection, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges multiple detection modalities (eye blink detection from movement sensors, brain activity analysis from EEG sensors, light exposure monitoring) into a unified sleep intention detection algorithm. This combining approach improves measurement precision by cross-validating multiple indicators while managing system complexity through integrated processing rather than separate independent systems
3Reliability
If traditional systems rely on subjective sleep intention reporting, then the ease of operation remains high, but the reliability of sleep onset latency determination deteriorates
Solution Approach 1:
The system employs self-service mechanisms where objective physiological indicators (eye blink cessation patterns, brain activity power spectral density changes, movement patterns) automatically determine sleep intention and onset without requiring user subjective reporting. This eliminates reliance on potentially unreliable user self-assessment while maintaining ease of operation as the system functions autonomously without requiring user input
Data Source
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AI summary
The present disclosure pertains to a system and method for determining sleep onset latency in a subject. The system is configured to generate output signals conveying information related to brain activity in the subject, determine sleep stages of the subject based on the output signals, determine a sleep onset moment in the subject based on the determined sleep stages, determine a sleep intention moment for the subject by: (i) detecting eye blinks in the subject based on the output signals, and determining the sleep intention moment responsive to the detected eye blinks ceasing for a predetermined period of time; and/or (ii) determining whether brain activity power in a target frequency band has breached a threshold power level based on the output signals, and determining the sleep intention moment responsive to a breach; and determine the sleep onset latency based on the sleep onset moment and the sleep intention moment.