Respiration Signal Segmentation for Sleep Apnea Screening
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Solution Overview
Problem
Current methods for diagnosing sleep apnea require invasive overnight monitoring with multiple sensors, which is cumbersome and not suitable for initial screening, especially for moderate-to-severe sleep apnea detection.
Innovation Solution
A system using a piezoelectric sensor strip placed under a mattress to acquire respiration signals, processed by a computing device for early screening, which includes segmentation, normalization, and machine learning-based classification to identify apnea/hypopnea events across multiple nights, providing a less-invasive preliminary assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor devices are used for sleep monitoring, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies multi-functionality by enabling a single sensor device to perform multiple functions: detecting respiration rate, determining sleep state (awake/asleep), and identifying sleep disorders. This allows one device to replace what would traditionally require multiple specialized sensors, thereby improving measurement precision across multiple parameters while reducing device complexity
Solution Approach 2:
The patent applies segmentation by dividing the monitoring function into distinct analytical stages: first determining whether the user is awake or asleep based on sensor data, then separately analyzing respiration rate and patterns. This segmented approach allows each analysis stage to focus on specific parameters, improving overall measurement precision while keeping the device design simpler than a system attempting to measure all parameters simultaneously
2Measurement precision
If invasive overnight monitoring is used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent uses the mattress as an intermediary medium that naturally integrates sensor devices into the sleep environment. The sensor devices are embedded within or attached to the mattress, allowing them to collect physiological data during normal sleep without requiring users to wear uncomfortable equipment or undergo clinical procedures. This intermediary approach maintains measurement precision for sleep apnea detection while dramatically improving ease of operation
Solution Approach 2:
The system applies self-service by automatically collecting and analyzing sleep data passively during normal sleep activities. The sensor devices continuously monitor physiological parameters without requiring user activation, adjustment, or intervention. The system autonomously determines sleep state, calculates respiration rate, and identifies potential sleep disorders, making the precise measurement process as convenient as sleeping itself
3Measurement precision
If multiple sensor types are used for diagnosis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies merging by integrating multiple sensor types (accelerometers, flex sensors, temperature sensors, etc.) into a single unified sensor strip embedded within the mattress. Rather than requiring separate devices for each measurement function, the patent combines these sensors into one integrated component that collectively captures respiratory patterns, body movements, and physiological data needed for accurate sleep apnea diagnosis while simplifying the overall system configuration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables a non-invasive, preliminary screening for sleep apnea severity, allowing for more accurate and efficient identification of moderate-to-severe cases, reducing the need for extensive overnight monitoring with multiple sensors.
Implementation Method 1
a sensor strip including one or more piezoelectric sensors placed underneath a sheet or mattress of the user
Data Source
AI summary
Sleep conditions such as sleep apnea can be assessed using a multi-night assessments. A respiration signal (e.g., acquired from a sensor strip) can be processed via a computing device. The respiration signal can be segmented and the segments can be classified to identify one or more apnea/hypopnea events. In some examples, some of the segments can be normalized such that each segment input for classification can be of the same size. The identified one or more apnea/hypopnea events can be used to estimate a nightly parameter indicative of a severity of (or presence of) sleep apnea. The nightly parameters from a multi-night period can be used to estimate a multi-night parameter indicative of the severity of (or presence of) sleep apnea. In some examples, quality checks can be performed to filter out some data (e.g., to exclude data from entire nights or exclude a portion of data from individual nights).