Active Medical Device for Selective Hypopnea Treatment
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Solution Overview
Problem
Current techniques for diagnosing and treating sleep-disordered breathing, particularly hypopneas, face challenges in real-time detection and targeted therapy, leading to excessive treatment and potential disruption of sleep, as they often apply therapy too late or incorrectly, resulting in reduced effectiveness over time.
Innovation Solution
An active medical device that analyzes and classifies ventilatory drops in real-time using multiple criteria, such as episode history, event severity, physiological origin, sleep stage, and treatment efficacy, to accurately identify hypopneas and apply tailored therapy only when necessary, thereby reducing unnecessary treatments and improving therapy relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If therapy is applied as soon as a respiratory drop is detected, then early treatment of hypopnea is achieved, but unnecessary treatment is applied to non-hypopnea events
Solution Approach 1:
The system performs preliminary classification of respiratory drops using multiple criteria (amplitude, duration, shape, episode history) before initiating therapy. This preliminary action filters out non-hypopnea events early in the detection process, preventing unnecessary treatment while maintaining early intervention for true hypopneas.
Solution Approach 2:
The system uses feedback from multiple sensor signals (respiratory flow, oxygen saturation, heart rate, movement) to continuously refine the classification of respiratory events. This multi-parameter feedback loop enables accurate real-time discrimination between hypopneas and non-hypopnea events, ensuring therapy is applied only when appropriate.
2Reliability
If all respiratory drop events are treated preventively, then potential hypopneas are treated early, but sleep is disrupted by excessive treatment
Solution Approach 1:
The system applies different treatment strategies to different types of respiratory events based on their classified characteristics. By analyzing local features of each respiratory drop (amplitude, duration, morphology, episode context), the system selectively applies therapy only to events meeting hypopnea criteria, avoiding sleep disruption from unnecessary treatment while maintaining comprehensive coverage for true hypopneas.
3Reliability
If therapy is applied after confirming hypopnea criteria, then treatment accuracy is improved, but treatment is delayed until after the event
Solution Approach 1:
The system performs preliminary classification using multiple criteria (amplitude threshold, duration threshold, episode history, signal morphology) to identify high-probability hypopnea events before confirmation. This allows early therapy initiation for events that meet multiple preliminary criteria, reducing treatment delay while maintaining high accuracy through the multi-criteria filtering process.
Data Source
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AI summary
The device includes means for measuring respiratory activity and detecting (20) an event of a drop in respiratory flow below a predetermined threshold. Discriminatory means (22, 40, 50, 70, 80, 90, 100, 102) analyze various parameters of this event in real time and determine whether they meet predefined criteria, so as to assign the event an indicator of a priori hypopnea. Anti-hypopnea therapy (28) is selectively triggered upon detection of the respiratory drop, but only in the event of a routine occurrence indicating a priori hypopnea.The criteria may include: history of intervals between successive events constituting episodes of hypopnea; severity of the current event; conformity of a profile of the current event with a reference profile; physiological, obstructive or central origin of the event; current sleep stage of the patient; and history of the degree of effectiveness of therapies applied.