Implantable Apnea Detection Using Moving Threshold Accumulation
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Solution Overview
Problem
Current methods for detecting the onset of apnea or hypopnea in patients with implantable medical devices are not prompt or reliable, often resulting in delayed therapy due to false positives and the need for extended periods of no respiration before detection, which can exacerbate underlying medical conditions like congestive heart failure.
Innovation Solution
An implantable medical system uses a moving threshold based on recent respiration cycles to detect the onset of apnea or hypopnea by accumulating differences in respiration amplitudes, allowing for real-time detection and immediate therapy delivery, utilizing thoracic impedance signals and existing cardiac pacing leads without additional sensors, and distinguishing between types of apnea for appropriate therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional apnea detection methods use extended periods of no respiration before detection, then false positives are reduced, but detection is delayed and therapy is not prompt
Solution Approach 1:
The system performs preliminary analysis of respiration patterns by continuously monitoring and comparing current respiration amplitude against historical data and thresholds before making a detection decision. This allows the system to prepare detection criteria in advance, enabling prompt detection when apnea occurs without requiring extended waiting periods after the event starts.
Solution Approach 2:
The system uses feedback from continuous monitoring of respiration amplitude and rate, comparing real-time measurements against established thresholds and patterns. This feedback mechanism allows the system to dynamically adjust detection decisions based on current physiological data, achieving both prompt detection and high reliability by confirming apnea through multiple data points rather than relying on extended waiting periods.
2Reliability
If conventional methods require extended periods of no respiration for detection, then false positives are reduced, but underlying conditions like CHF are exacerbated due to delayed therapy
Solution Approach 1:
The system prepares detection criteria and continuously pre-processes respiration data before apnea occurs, allowing immediate detection and therapy initiation when apnea is detected, rather than waiting for extended periods. This preliminary preparation reduces detection delay and prevents exacerbation of CHF and other conditions.
Solution Approach 2:
The system implements continuous feedback monitoring of respiration parameters, enabling real-time detection of apnea events. This immediate feedback loop allows therapy to be delivered promptly upon detection, preventing the delayed therapy that would otherwise exacerbate underlying medical conditions like congestive heart failure.
3Speed
If real-time detection is implemented, then therapy can be delivered immediately, but detection reliability may decrease due to false positives
Solution Approach 1:
The system uses multi-parameter feedback including respiration amplitude, respiration rate, and comparison against historical patterns to confirm apnea detection. This feedback mechanism allows real-time detection speed while maintaining reliability by requiring multiple confirming data points before triggering therapy, thereby reducing false positives.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on individual patient physiology and historical data. By customizing detection criteria for each patient, the system achieves real-time detection reliability without excessive false positives, as the parameters are optimized for the specific patient's normal respiratory patterns.
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 prompt and reliable detection of apnea or hypopnea episodes, initiating appropriate therapy before significant drops in respiration amplitude occur, thereby reducing adverse effects on patients with conditions like congestive heart failure.
Implementation Method 1
monitoring a respiration parameter such as respiration amplitude determined from thoracic impedance
Data Source
AI summary
Techniques are provided for detecting the onset of an episode of apnea/hypopnea substantially in real-time. A moving threshold is generated based on recent respiration cycles and differences are accumulated between amplitudes of new respiration cycles and the moving threshold. Apnea/hypopnea is then detected based upon a comparison of the accumulated differences against a fixed threshold. The technique exploits the fact that many episodes of hypopnea begin with a sharp drop in respiration and many episodes of apnea are preceded by a sharp drop in respiration. By accumulating differences between new respiration amplitudes and a short term moving average, any sharp drop in respiration is thereby promptly detected. In many cases, by the time the amplitudes of individual respiration cycles drop to levels directly indicative of apnea/hypopnea, the episode of apnea/hypopnea will have already been detected based upon the sudden sharp drop in respiration amplitude and therapy will have already been initiated.