EMG Device Design for Muscle-Based Sleep Stage Estimation
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Solution Overview
Problem
Conventional polysomnography (PSG) for sleep stage estimation is costly, uncomfortable for subjects due to multiple sensor attachments, and requires overnight stays in sleep laboratories, making it inconvenient and disruptive.
Innovation Solution
A computer-implemented method using electromyography (EMG) signals, incorporating both muscle activity and physiological parameters like heart rate and breathing, to train a machine learning model for sleep stage estimation, allowing sensors to be placed conveniently without the need for additional attachments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography (PSG) is used for sleep stage estimation, then measurement precision is improved, but device complexity and subject comfort deteriorate due to multiple sensors and wires
Solution Approach 1:
The patent extracts and emphasizes the EMG signal component from the complex polysomnography system, demonstrating that sleep stage estimation can be achieved using only EMG signals without requiring EEG, EOG, or other modalities. This extraction simplifies the system while maintaining measurement precision for sleep stage classification.
Solution Approach 2:
The EMG signal is shown to serve multiple functions: it provides both muscle activity information and physiological parameter information (heart rate, breathing) that can be used for sleep stage estimation. This multi-functionality eliminates the need for separate sensors for each function, reducing overall device complexity.
2Measurement precision
If polysomnography (PSG) is used for sleep stage estimation, then measurement precision is improved, but ease of operation deteriorates due to subject discomfort and inconvenience
Solution Approach 1:
The patent extracts the essential EMG signal from the complex PSG system, eliminating the need for multiple sensors and wires that cause discomfort. By using only EMG signals, the system maintains measurement precision while significantly improving subject comfort and ease of operation.
Solution Approach 2:
The patent uses machine learning models to process and interpret EMG signals, creating a simplified digital representation of sleep stages without requiring physical attachments to the subject. This copying approach maintains accuracy while eliminating the physical discomfort of multiple sensors.
3Measurement precision
If polysomnography (PSG) is used for sleep stage estimation, then measurement precision is improved, but loss of time increases due to overnight stay in sleep laboratory
Solution Approach 1:
The patent enables home-based sleep monitoring where subjects can perform sleep studies in their own environment using simplified EMG-based systems. This self-service approach eliminates the need for overnight stays in sleep laboratories, allowing subjects to monitor their sleep at home without sacrificing measurement precision.
Solution Approach 2:
The patent replaces the mechanical PSG system with a simplified EMG-based system that can be used in home environments. This substitution maintains the ability to accurately estimate sleep stages while eliminating the time loss associated with traveling to and staying in sleep laboratories.
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 accurate sleep stage estimation with improved subject comfort by using EMG signals alone, reducing the need for multiple sensors and facilitating convenient home-based sleep monitoring.
Implementation Method 1
a sensor adapted to generate an EMG signal representative of electric activity of a muscle of the subject
Data Source
AI summary
A computer-implemented method for estimating sleep stages that comprises receiving an electromyography (EMG) signal representative of electric activity of a muscle of the subject during the sleep session and providing only the EMG signal as an input to a machine learning model. The method comprises estimating a sleep stage during the sleep session based on an output of the machine learning model. The machine learning model is trained by providing, as a first input, a reference EMG signal representative of electric activity of a muscle of a reference subject during a reference sleep session, and providing, as a second input, a reference sleep stage signal representative of sleep stages of the reference subject during the reference sleep session. The machine learning model is trained by using the first input and the second input to estimate sleep stages based on only an EMG signal.