Integrated CPAP Sensors for Stroke-Onset Alerts During Sleep
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Solution Overview
Problem
Current technologies for stroke diagnosis, such as CT, MRI, and blood measurements, are not suitable for early detection and alerting of stroke onset during sleep, especially in home settings, and wearing additional devices on the head during sleep is not appealing to patients.
Innovation Solution
A CPAP system integrated with sensors and machine learning algorithms to monitor vital signs and physical symptoms, including HRV, HR, arrhythmias, and facial asymmetry, to detect the onset of stroke and generate alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current standard stroke diagnosis technologies (CT, MRI, Ultrasound) are used, then stroke detection accuracy is improved, but device complexity and ease of operation deteriorate due to hospital setting requirements and inability to detect during sleep
Solution Approach 1:
The patent replaces complex mechanical imaging systems (CT, MRI) with a wearable CPAP device that uses sensors to detect stroke symptoms through physiological parameters like heart rate variability, breathing patterns, and facial movements, enabling home-based detection during sleep
Solution Approach 2:
The CPAP device is enhanced with multiple sensor types (accelerometers, heart rate monitors, breathing sensors) that serve both sleep apnea treatment and stroke detection functions, making the device universally applicable for both purposes without requiring separate equipment
2Reliability
If additional head-worn devices are added for stroke monitoring, then stroke detection capability is improved, but device complexity and patient compliance deteriorate
Solution Approach 1:
The patent merges stroke monitoring functionality with the existing CPAP device by integrating sensors into the CPAP mask and headgear, eliminating the need for separate head-worn monitoring devices and reducing overall system complexity
Solution Approach 2:
The CPAP device is enhanced with multiple sensor types (accelerometers, heart rate monitors, breathing sensors) that serve both sleep apnea treatment and stroke detection functions, making the device universally applicable for both purposes without requiring separate equipment
3Loss of time
If stroke symptoms are monitored during sleep, then early detection timing is improved, but measurement precision deteriorates due to reduced physiological activity during sleep
Solution Approach 1:
The system continuously monitors physiological parameters during sleep and uses feedback algorithms to detect stroke symptoms, comparing real-time data against baseline values to identify deviations that indicate stroke onset, thereby maintaining detection precision despite reduced physiological activity
Solution Approach 2:
The patent replaces complex mechanical imaging systems (CT, MRI) with a wearable CPAP device that uses sensors to detect stroke symptoms through physiological parameters like heart rate variability, breathing patterns, and facial movements, enabling home-based detection during sleep
Data Source
AI summary
A method (84) for stroke onset alert for a continuous positive airway pressure (CPAP) system (12) comprises providing (86), via a blower (14) and a patient circuit (16), a pressurized flow of breathable gas to a patient interface device (18) configured for being worn by a subject (20). The method includes monitoring and recording (88), via a monitoring unit (28) integral with the patient interface device, a predetermined set of vital signs and physical symptoms of the subject relating to an onset of acute stroke attack or stroke episode during the delivery of the pressurized flow of breathable gas. In addition, the method comprises identifying (90), via a stroke onset detection module (34), an onset of stroke based on an analysis of the monitored and recorded set of vital signs and physical symptoms and outputting (92), via a stroke onset alert module (36), a stroke onset alert signal based on the identification of the onset of stroke via the stroke onset detection module.


