REM Sleep Detection Using Poincare Plot Analysis of Inter-Beat Intervals
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Solution Overview
Problem
Current sleep analysis methods, such as polysomnography, require multiple physiological inputs and are labor-intensive, making them inefficient for accurately monitoring sleep stages like REM sleep without an electrocardiogram (ECG) signal.
Innovation Solution
A method and system that receive non-ECG signals indicative of heart beats, extract inter-beat intervals, calculate Poincare parameters, and use time-frequency decomposition to determine REM sleep stages, allowing for automated analysis without the need for multiple ECG leads or manual processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography with multiple physiological inputs is used, then sleep stage analysis accuracy is improved, but device complexity and labor intensity increase
Solution Approach 1:
The invention extracts and utilizes only the ECG signal from the multiple physiological inputs required by traditional polysomnography. By focusing on the R-R intervals within the ECG signal and applying Poincare plot analysis, the method achieves sleep stage detection without requiring EEG, EMG, EOG, or other additional physiological inputs, thereby simplifying the device while maintaining diagnostic accuracy
Solution Approach 2:
The ECG signal is made multi-functional by using it not only for cardiac monitoring but also for sleep stage detection. The R-R interval variations within the ECG signal contain information about sleep stages, allowing a single signal to serve multiple diagnostic purposes and eliminate the need for separate sensors for each physiological parameter
2Measurement precision
If manual off-line analysis of PSG data is performed, then detailed sleep stage classification is achieved, but productivity and efficiency decrease
Solution Approach 1:
The invention implements automated real-time feedback analysis of ECG signals using Poincare plot parameters. The system continuously calculates SD1 and SD2 parameters from R-R intervals and automatically classifies sleep stages based on predefined thresholds, providing immediate feedback without requiring manual review of recorded data, thus dramatically improving analysis productivity while maintaining classification accuracy
Solution Approach 2:
The system performs self-service automated analysis by automatically detecting R-peaks, calculating inter-beat intervals, generating Poincare plots, computing statistical parameters, and classifying sleep stages without human intervention. This automation eliminates the labor-intensive manual scoring process while maintaining the diagnostic detail previously achievable only through expert manual analysis
3Measurement precision
If traditional ECG-based methods are used for sleep analysis, then REM sleep detection is possible, but the requirement for multiple ECG leads increases device complexity
Solution Approach 1:
The invention extracts the essential information for REM detection by focusing solely on the R-R interval variations within a single ECG lead. By analyzing the temporal patterns of heart rate variability during different sleep stages, the method achieves REM detection without requiring multiple ECG leads or additional sensor placements, thereby simplifying the hardware configuration
Data Source
AI summary
A method of analysis is disclosed. The method comprises receiving a non-ECG signal indicative of heart beats of a sleeping subject; extracting from the signal a series of inter-beat intervals (IBI); calculating at least one Poincare parameter characterizing a Poincare plot of the IBI series; and using the Poincare parameter(s) to determine a REM sleep of the sleeping subject. In some embodiments, sleep stages other than REM sleep and/or wake stages are determined.