Sleep Intervention Quality Assessment Using Multi-Parameter Metrics
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Solution Overview
Problem
Traditional techniques for measuring brain activity during sleep are limited in assessing the efficacy of sleep intervention strategies effectively.
Innovation Solution
Systems and methods that receive and process biological parameter measurements before and after a sleep intervention, generating quality assessment metrics and reports, including biomarkers and prediction models, to evaluate the effectiveness of sleep interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional measurement techniques are used to measure brain activity during sleep, then the measurement process is simple, but the ability to assess sleep intervention efficacy is limited
Solution Approach 1:
The patent segments the sleep intervention assessment into multiple independent quality metrics including sleep quality metric, alertness metric, relaxation metric, and cognitive performance metric. Each metric is calculated from specific physiological parameters (brain activity, heart rate, respiration, eye movement) to provide comprehensive yet modular evaluation of intervention effectiveness.
Solution Approach 2:
The patent transitions from traditional single-dimension sleep measurement to multi-dimensional assessment by incorporating physiological data across multiple frequency bands (delta, theta, alpha, beta, gamma) and multiple physiological systems (neural, cardiac, respiratory, ocular) to evaluate sleep intervention efficacy from diverse perspectives.
2Measurement precision
If multiple physiological parameters are measured to improve assessment accuracy, then the assessment quality improves, but the complexity of data processing increases
Solution Approach 1:
The patent extracts specific meaningful features from raw physiological signals by calculating quality metrics based on power spectral density in different frequency bands, heart rate variability, respiration rate, and eye movement patterns. This extraction process transforms complex continuous signals into discrete quantifiable metrics that are easier to process and interpret.
Solution Approach 2:
The patent performs preliminary processing of physiological data during the measurement phase by continuously monitoring and pre-calculating quality metrics from incoming signals. This preliminary action prepares the data in advance, reducing the computational burden during final analysis and enabling real-time assessment of sleep intervention effects.
Data Source
AI summary
Provided are systems, methods, and devices for sleep intervention quality assessment. Methods include receiving measurement data from a plurality of data sources, the measurement data comprising a plurality of measurements of biological parameters of a user before and after a sleep intervention, and receiving treatment data comprising one or more treatment parameters associated with the sleep intervention. Methods further include generating, using one or more processors, a plurality of quality assessment metrics based on the received measurement data, the plurality of quality assessment metrics being generated based, at least in part, on a comparison of the plurality of measurements of biological parameters before and after the sleep intervention, and generating a report based, at least in part, on the plurality of quality assessment metrics.