Wearable ECG Sleep Apnea Classification Using Machine Learning

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Solution Overview

Problem

Conventional sleep apnea diagnosis methods, such as polysomnography, are logistically and financially burdensome, and home sleep tests often fail to accurately differentiate between types of sleep apnea or provide meaningful insights, leading to underdiagnosis and misattribution of symptoms.

Innovation Solution

A wearable monitoring device captures electrocardiogram data over extended periods using machine learning models to analyze heart activity and additional physiological signals, enabling accurate classification of sleep apnea types and severity, including obstructive and central sleep apnea, through a non-invasive and personalized approach.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If polysomnography is used for sleep apnea diagnosis, then diagnostic accuracy is improved, but logistical complexity and cost increase

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidlogistical complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts the essential diagnostic function from complex polysomnography equipment by using a simplified wearable device that monitors only electrocardiogram signals. This extraction maintains diagnostic accuracy for sleep apnea detection while removing unnecessary complexity of full polysomnography setups including multiple sensors and sleep lab requirements

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent replaces the mechanical and complex sensor system of polysomnography with an electrocardiogram-based detection system. By substituting the mechanical monitoring approach with electrical signal analysis through machine learning models, the system achieves accurate sleep apnea diagnosis without the logistical burden of traditional polysomnography equipment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If home sleep tests are used to increase accessibility, then ease of operation is improved, but measurement precision deteriorates

Engineering Contradiction:

ImproveaccessibilityVSAvoiddiagnostic accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces conventional home sleep test mechanisms with an electrocardiogram-based system enhanced by machine learning. This substitution enables the device to differentiate between obstructive and central sleep apnea types, providing diagnostic accuracy comparable to polysomnography while maintaining the accessibility and ease of use of home-based testing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the measurement parameter from conventional home sleep test signals to electrocardiogram parameters analyzed through machine learning models. This parameter change enables the system to extract meaningful diagnostic information including sleep apnea type classification and severity assessment, thereby improving measurement precision while maintaining accessibility

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If conventional home sleep tests are used, then ease of operation is improved, but ability to differentiate sleep apnea types deteriorates

Engineering Contradiction:

ImproveaccessibilityVSAvoidinformation about sleep apnea type

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The patent replaces conventional home sleep test analysis with machine learning models that process electrocardiogram signals. This substitution enables differentiation between obstructive and central sleep apnea types by analyzing specific patterns in the ECG data, thereby recovering the lost information about sleep apnea classification while maintaining the accessibility of home-based testing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces machine learning models as an intermediary between the raw electrocardiogram signals and the diagnostic output. This intermediary processes the signals to extract meaningful information about sleep apnea type and severity, enabling the system to provide comprehensive diagnostic information that conventional home sleep tests cannot deliver

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250302380A1Sleep Apnea Prediction Using Electrocardiograms and Machine Learning

Publication Date: 2025.10.02 IRHYTHM TECHNOLOGIES INC

AI summary

Sleep apnea prediction using electrocardiograms and machine learning is described. In one or more implementations, a wearable monitoring device produces electrical potential measurements of a heart of a user during an observation period spanning multiple days. A sleep apnea classification of the user is predicted by providing the electrical potential measurements to one or more machine learning models as input. The one or more machine learning models are trained based on historical electrical potential measurements and historical outcome data of a user population to correlate patterns in electrical potential measurements to sleep apnea classifications. The sleep apnea classification may then be output, such as in a health report, via a user interface, as notification on a computing device, and so forth.