Sleep Apnea Detection via RR Interval Autocorrelation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing sleep apnea, such as polysomnographic examinations, are cumbersome, costly, and not suitable for frequent monitoring due to the need for long-term Holter device use, which is stressful for patients and requires significant data storage and processing.
Innovation Solution
A method utilizing instantaneous heartbeat rate analysis through RR interval measurements from ECG or IEGM signals, employing autocorrelation functions to detect pathological oscillations indicative of sleep apnea, suitable for integration into cardiological implants and telemetry systems, allowing for reduced effort and more frequent monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnographic examination with long-term Holter device is used, then measurement precision for sleep apnea diagnosis is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The invention extracts only the essential feature needed for sleep apnea detection from the complex polysomnographic examination - specifically, analyzing RR interval variations from standard ECG data. This extraction allows diagnosis using simple cardiological monitoring equipment already present in implantable devices, eliminating the need for complex external Holter devices and polysomnography equipment while maintaining diagnostic accuracy
Solution Approach 2:
The invention makes implantable cardiological devices multi-functional by enabling them to perform both their primary cardiac monitoring function and sleep apnea detection using the same ECG/IEGM data and processing resources. This eliminates the need for separate specialized diagnostic equipment, reducing overall device complexity and making the solution universally applicable to existing cardiac implantables
2Measurement precision
If long-term Holter device monitoring is used, then measurement precision is improved, but loss of time and productivity worsen
Solution Approach 1:
The invention performs preliminary action by continuously analyzing RR interval variations in real-time during routine cardiac monitoring, so that sleep apnea events are detected as they occur or are identified retrospectively from already-collected data. This eliminates the need for time-consuming separate diagnostic sessions and allows prompt detection without additional time loss
Solution Approach 2:
The implantable device performs self-service by automatically detecting sleep apnea events using its own ECG/IEGM data and processing capabilities without requiring external equipment or specialist intervention. The device autonomously identifies pathological oscillations in RR intervals and generates diagnostic information, eliminating the need for external Holter device analysis sessions
3Measurement precision
If standard polysomnographic methods are used, then measurement precision is improved, but ease of operation and device complexity worsen due to data storage and processing requirements
Solution Approach 1:
The invention extracts only the critical diagnostic feature - RR interval variations - from the ECG/IEGM signal, ignoring all other polysomnographic parameters. This extraction reduces data processing to simple temporal analysis of heartbeat intervals, eliminating the need for complex multi-parameter analysis while maintaining diagnostic accuracy for sleep apnea
Solution Approach 2:
Instead of analyzing complex respiratory and neurological signals to detect sleep apnea, the invention inverts the approach by analyzing cardiac rhythm variations (RR intervals) as the primary indicator. This inversion simplifies data processing because cardiac data is already continuously available from implantable devices and requires minimal processing compared to traditional polysomnographic methods
Data Source
AI summary
Methods and apparatuses for detecting sleep apnea by analyzing characteristic physiological oscillations of the heart rate variability (HRV). Starting from recorded ECG data of the patient, for example, as a long-term sequence of the changing RR intervals, the heart rate variability is examined using autocorrelation calculations for the occurrence of rhythmic oscillations of various frequencies. If oscillations typical for apnea occur having very long period durations in the range of 20 to 80 seconds, these are detected as a maximum of the autocorrelation function. If a pathological sleep apnea accordingly exists, individual apnea events may be identified by prompt analysis of short recorded RR sequences, e.g., in the minute interval.