Cardiac Signal Sleep Apnea Detection Using Heart Rate Cycles
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Solution Overview
Problem
Existing sleep apnea detection algorithms based on cardiac signals have a significant false positive error rate, leading to inadequate treatment of the condition.
Innovation Solution
A method and system that utilize cardiac signal analysis to determine short-term and long-term heart rate averages, peak-to-valley time intervals, and specific conditions to accurately detect sleep apnea episodes by monitoring heart rate cycles and variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sleep apnea detection algorithms based on cardiac signals are used, then sleep apnea episodes can be detected, but the false positive error rate is significant
Solution Approach 1:
The patent segments the heart rate data into multiple time windows (short-term and long-term averaging windows) to analyze different temporal patterns. By dividing the analysis into distinct time segments with different averaging periods, the algorithm can distinguish between transient heart rate variations and sustained patterns indicative of sleep apnea, thereby reducing false positives while maintaining detection accuracy.
2Measurement precision
If short-term and long-term heart rate averages are calculated with multiple time windows, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential features needed for sleep apnea detection from the cardiac signal - specifically focusing on heart rate variations and their temporal patterns. By extracting and analyzing only the relevant short-term and long-term average heart rate differences, the algorithm achieves high detection accuracy without requiring complex analysis of the entire cardiac signal, thus managing computational complexity effectively.
Data Source
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AI summary
A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.