IoT AED ECG Classification via Remote Server
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Solution Overview
Problem
Current automated external defibrillators (AEDs) face challenges in utilizing advanced shock advice algorithms (SAAs) due to limited computational resources and power constraints, which hinders their ability to accurately analyze electrocardiogram (ECG) measurements.
Innovation Solution
An internet of things connected AED that transmits ECG measurements to a remote processing device, such as a cloud server, for classification using advanced SAAs, while also employing a local SAA for independent classification and immediate decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced shock advice algorithms (SAAs) based on deep neural networks are implemented in AEDs, then measurement precision and classification accuracy are improved, but device complexity and power consumption increase beyond the capabilities of low-power chips
Solution Approach 1:
The patent introduces a remote server as an intermediary to perform the computationally intensive ECG classification tasks. The AED device captures ECG measurements and transmits them to the remote server, which runs the advanced deep neural network-based SAA and returns the classification result. This mediator approach allows the use of complex algorithms without increasing the computational burden on the AED's low-power chip.
Solution Approach 2:
The patent replaces the mechanical/computational processing system within the AED with a remote cloud-based processing system. Instead of running advanced SAAs locally on the AED's hardware, the system substitutes local computational mechanics with remote server-side processing, leveraging the superior computational resources available in the cloud environment.
2Measurement precision
If advanced shock advice algorithms are implemented in AEDs, then measurement precision is improved, but use of energy increases beyond the limited battery supply
Solution Approach 1:
The remote server acts as an intermediary that handles the energy-intensive classification computations. The AED device minimizes its energy consumption by only performing ECG data acquisition and transmission, while delegating the power-hungry deep neural network inference to the remote server. This division of computational labor allows high-precision classification without exceeding the AED's limited battery capacity.
Solution Approach 2:
The patent extracts the energy-consuming classification functionality from the AED device and relocates it to a remote server. By separating the measurement function (performed by the AED) from the classification function (performed by the server), the system achieves accurate ECG analysis while keeping the AED's power consumption within acceptable limits.
3Measurement precision
If ECG measurements are transmitted to remote processing devices for classification, then measurement precision is improved, but loss of time occurs during network transmission
Solution Approach 1:
The system maintains continuous monitoring by having the AED continuously capture ECG measurements and maintain readiness to transmit. The remote server continuously processes incoming data and provides rapid classification. This continuous operation minimizes idle time and ensures that when transmission occurs, the system is already prepared, reducing the effective decision-making time despite network latency.
Solution Approach 2:
The AED device performs preliminary ECG data acquisition and preprocessing before transmission, ensuring the data is ready for immediate analysis upon receipt by the remote server. The system also maintains pre-established communication channels and protocols, so that when classification is needed, the transmission can occur without setup delays, minimizing the overall time loss.
Data Source
AI summary
Provided is an Automated External Defibrillator AED and a server. The AED makes an ECG measurement of a heart and transmits the measurement to a remote processing device, for example the server, and receives back from the server a first classification of the ECG measurement. The AED also performs a local analysis of the ECG measurement to determine a second classification of the ECG measurement. The AED determines whether to administer a shock/instruct a user to administer a shock based on the first and second classifications.