Peripheral Arterial Tone Signal Processing for VAR-Aware Sleep Event Detection
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Solution Overview
Problem
Conventional plethysmography methods struggle to accurately detect sleep-disturbing events due to the interference of venoarteriolar reflex (VAR) caused by hydrostatic pressure gradients, leading to indeterminate vasoconstriction and inaccurate detection of sleep disorders.
Innovation Solution
A method and apparatus using conventional medical equipment to process peripheral arterial tone (PAT) signals, accounting for venoarteriolar reflex effects by deriving reference and baseline amplitude values to quantify vasoconstriction events, and employing classifiers to detect sleep-disturbing events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional plethysmography methods are used to monitor peripheral arterial tone, then the measurement can be performed with simple equipment, but the detection accuracy is reduced due to interference from venoarteriolar reflex
Solution Approach 1:
The patent introduces a baseline-invariant signal as an intermediary representation that separates the venoarteriolar reflex component from the true peripheral arterial tone signal. By dividing the raw PPG signal by the baseline signal, the method creates a normalized representation that eliminates the confounding VAR effect, allowing accurate detection of sleep-disturbing events without requiring complex hardware modifications.
Solution Approach 2:
The patent transforms the raw PPG signal into a baseline-invariant signal by changing the parameter representation from absolute amplitude to normalized amplitude ratio. This parameter transformation removes the influence of hydrostatic pressure gradients and venoarteriolar reflex, converting the signal into a form that accurately reflects only the peripheral arterial tone changes caused by sleep disturbances.
2Productivity
If venoarteriolar reflex effects are not accounted for, then the signal processing is simpler, but false positives and negatives increase in sleep-disturbing event detection
Solution Approach 1:
The patent performs preliminary signal processing by dividing the raw PPG signal by the baseline signal to create a baseline-invariant representation before any sleep disturbance detection algorithms are applied. This preliminary action removes the confounding VAR effect in advance, ensuring that subsequent detection algorithms operate on cleaned data that accurately reflects true peripheral arterial tone changes, thereby reducing false positives and negatives.
Solution Approach 2:
The patent incorporates feedback by continuously monitoring the baseline signal and using it to normalize the raw PPG signal in real-time. The baseline-invariant signal generation process feeds back the baseline information into the detection algorithm, allowing the system to adapt to changing baseline conditions and maintain accurate detection reliability throughout the monitoring period.
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
This approach significantly reduces false positives and negatives in sleep-disturbing event detection by accounting for steady-state changes, ensuring accurate and robust identification of sleep disorders.
Implementation Method 1
Optical plethysmography or photoplethysmography measures the arterial blood volume changes by shining light from one or more light sources, such as LEDs, onto an investigated volume and by detecting collected light corresponding to the light being reflected or transmitted in the investigated volume on a sensor
Data Source
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AI summary
The present disclosure relates to a method and apparatus for detecting sleep-disturbing events from a signal indicative of a peripheral arterial tone of an individual, the signal being affected by a venoarteriolar reflex, wherein sleep-disturbing events are detected by: determining a vasoconstriction event from changes in the signal; deriving, a reference amplitude value and a baseline amplitude value for the vasoconstriction event, the reference amplitude value being different from the baseline amplitude value; relating the reference amplitude value for the vasoconstriction event to the baseline amplitude value for the vasoconstriction event, thereby obtaining a magnitude measure for the vasoconstriction event; and detecting, therefrom, a sleep-disturbing event.