Adaptive PAP Therapy System Using Biosensor Feedback
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Solution Overview
Problem
Conventional PAP therapies for sleep apnea are intrusive, noisy, and uncomfortable, leading to low compliance due to their inability to dynamically adjust airway pressure in real-time based on individual sleep quality.
Innovation Solution
An adaptive positive airway pressure therapy system that utilizes a biosensor system to monitor biological signals, including EEG, EKG, EMG, and oximeter data, to adjust airway pressure in real-time through a machine learning model, optimizing sleep quality and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional PAP machines deliver fixed pressure to maintain airway patency, then airway obstruction is reduced, but user comfort and compliance deteriorate due to noise and intrusiveness
Solution Approach 1:
The system transitions from fixed pressure delivery to dynamic, real-time pressure adjustment based on detected sleep stages and respiratory events. The controller continuously modulates pressure levels responding to EEG, EKG, and respiratory monitoring data, making the therapy adaptive rather than static.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring biological signals (EEG for sleep stages, EKG for heart rate, respiratory effort) and using this information to adjust pressure settings. The controller receives real-time data and automatically modifies therapy parameters to maintain comfort and effectiveness.
2Reliability
If PAP machines use high pressure to ensure airway patency, then treatment efficacy is improved, but sleep quality deteriorates due to discomfort and noise
Solution Approach 1:
The system dynamically adjusts pressure levels based on real-time detection of sleep stages and respiratory events. During lighter sleep stages or when respiratory events are detected, pressure increases to maintain efficacy. During deeper sleep stages, pressure is reduced to minimize disturbance, creating a dynamic balance between treatment efficacy and sleep quality.
Solution Approach 2:
The system changes multiple parameters including pressure level, pressure ramp rate, and alarm thresholds based on detected sleep stages and respiratory events. This multi-parameter adaptation allows the system to maintain treatment efficacy while minimizing harmful effects like noise and discomfort during different sleep phases.
3Device complexity
If conventional PAP devices lack real-time adjustment capability, then device simplicity is maintained, but adaptability to individual sleep patterns deteriorates
Solution Approach 1:
The system performs self-adjustment by automatically processing biological signals and modifying therapy parameters without manual intervention. The controller continuously monitors sleep stages and respiratory events, then autonomously adjusts pressure settings to match individual sleep patterns, eliminating the need for manual reconfiguration.
Solution Approach 2:
The system replaces manual mechanical adjustment with automated electronic control. Instead of requiring users to manually adjust pressure settings, the system uses sensors, processors, and algorithms to automatically detect sleep stages and adjust therapy parameters, substituting mechanical simplicity with intelligent automation.
Data Source
AI summary
A method of controlling a positive airway pressure (PAP) therapy device involves iteratively performing the actions of receiving at least one biosensor data from at least one biosensor system from a subject utilizing a PAP therapy device during a therapy session. The method receives air pressure reading from a pressure sensor of the PAP therapy device. The method converts the at least one biosensor data into sliding window time-series data. The method packages the sliding window time-series data and air pressure reading into a training set for a classification model to identify sleep quality biomarkers. The method generates an air pressure control through operation of a heuristic model configured by the sleep quality biomarkers. The method adjusts positive airway pressure generated by a blower motor. The method assigns a sleep quality score to the therapy session upon detecting the conclusion of the therapy session and retrains the heuristic model.