Sleep Arousal Detection Via Sleep-Stage Probability Continuity
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Solution Overview
Problem
Existing methods for detecting arousals during sleep, such as EEG, are intrusive and provide less accurate results, while other physiological measures are either obtrusive or not sufficiently precise.
Innovation Solution
A computer-implemented method using probability distributions of sleep stage classes over multiple epochs to detect arousals by analyzing the continuity metric between these distributions, allowing for non-invasive and accurate arousal detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG is used to measure arousals, then measurement precision is improved, but ease of operation deteriorates due to obtrusive electrodes
Solution Approach 1:
The patent uses probability distributions from less obtrusive physiological measurements (respiratory signals, heart rate variability, muscle activity) to copy or replicate the arousal detection capability of EEG. By analyzing changes in probability distributions of sleep stage classes from these alternative signals, the system achieves accurate arousal detection without requiring EEG electrodes attached to the scalp.
2Ease of operation
If less obtrusive physiological measures are used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the approach by changing from direct arousal measurement to measuring probability distributions of sleep stage classes. By calculating continuity metrics that represent differences between probability distributions at different time points, the system extracts arousal information from physiological signals in a way that maintains measurement precision while improving ease of operation.
Solution Approach 2:
The patent applies different processing approaches to different aspects of the physiological data. Instead of treating all physiological signals uniformly, the system analyzes probability distributions of sleep stage classes separately and combines them through continuity metric calculations, allowing each signal type to contribute its specific quality to the overall arousal detection.
Data Source
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AI summary
There is provided a computer-implemented method for detecting an arousal in a sleep session of a subject. The computer-implemented comprises receiving a first signal comprising information about a first probability distribution of sleep stage classes during the first epoch. The computer-implemented comprises receiving a second signal comprising information about a second probability distribution of the sleep stage classes during the second epoch. The computer-implemented comprises determining a continuity metric representative of a difference between the first probability distribution and the second probability distribution. The computer-implemented comprises determining a presence of an arousal based on the continuity metric. Further, there is provided an arousal detection system for detecting an arousal in a sleep session of a subject. The arousal detection system comprises a processing system and a sensor interface. The processing system is configured to perform the computer-implemented method.