Wearable Arrhythmia Monitoring With Cloud Classifiers to Reduce False Alerts
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Solution Overview
Problem
Existing wearable devices for cardiovascular monitoring suffer from high false-positive and false-negative rates in detecting events like atrial fibrillation, leading to unnecessary prompts and delayed medical treatment, due to limited data processing capabilities and the need for extensive data storage and processor power that is impractical in wearable devices.
Innovation Solution
A wearable device that uploads sensor data to a cloud computing service for generating and updating a cardiovascular classifier using extensive data from multiple sources, including clinical systems and other wearable devices, to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive data storage and high processor power are used to improve detection accuracy, then false-positive and false-negative rates are reduced, but device complexity and power consumption increase making wearable implementation impractical
Solution Approach 1:
The patent introduces a cloud computing service as an intermediary between the wearable device and the data processing requirements. The wearable device collects sensor data and transmits it to the cloud service, which performs the computationally intensive classifier generation and updating. This mediator approach allows the wearable device to achieve high detection accuracy without requiring complex onboard processing capabilities or large data storage, thereby resolving the contradiction between reliability and device complexity.
2Reliability
If continuous monitoring is performed to improve detection of rare events, then detection accuracy increases, but power consumption and data processing requirements increase
Solution Approach 1:
The patent segments the monitoring system into two parts: a lightweight wearable device that performs only data collection and transmission, and a remote server that performs all computationally intensive analysis. This segmentation allows the wearable device to maintain continuous monitoring with minimal power consumption, while the energy-intensive processing is offloaded to the remote server, resolving the contradiction between detection accuracy and power consumption.
3Measurement precision
If more sensor data is collected to improve classifier accuracy, then detection precision improves, but data transmission and storage requirements increase
Solution Approach 1:
The cloud computing service acts as an intermediary that receives and manages large volumes of sensor data from multiple wearable devices. This mediator capability allows the system to collect and process extensive data from multiple sources to generate highly accurate cardiovascular classifiers, while the wearable devices themselves do not need to store or manage large data volumes, resolving the contradiction between measurement precision and data quantity.
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
Reduces false-positive and false-negative detections by leveraging cloud-based data processing, enabling more accurate and timely detection of cardiovascular events, facilitating prompt medical intervention.
Implementation Method 1
detecting an electrocardiographic waveform from voltage fluctuations between the first electrical contact and the second electrical contact
Data Source
AI summary
Systems and methods are provided for continuously monitoring a user to determine when cardiovascular events are likely occurring and to responsively provide a prompt to a user to engage in additional physiological assessment of the putative cardiovascular event. Additional assessment can include the user engaging an additional sensor to provide signals that are more accurate, lower noise, or otherwise improved relative to a continuously-monitoring sensor used to initially detect the cardiovascular event. Detection of cardiovascular events includes using a cardiovascular classifier to determine, based on the output of such a continuously-monitoring sensor, whether the event is likely occurring. Such a classifier can be received from a cloud computing service or other remote system based on sensor outputs sent to such a system. Use of such a classifier can facilitate reduced false-positive detection of cardiovascular events based on the continuously-monitoring sensor, and thus reduce extraneous prompts to the user.