Highly optimised, this AI model makes it possible to run various real-time heart monitoring applications to help users and their healthcare providers quickly identify any irregular events enabling any necessary actions to be taken quickly. As with all Ambiq Model Zoo components, the HeartKit includes scripts and tools to help AI developers add real-time ECG monitoring capabilities to their health-tech applications.
Personalised health monitoring is becoming ubiquitous with the development of AI models, spanning clinical-grade remote patient monitoring to commercial-grade health and fitness applications.
Most leading consumer products offer similar electrocardiograms (ECG) for common types of heart arrhythmia. Ambiq’s HeartKit is a reference AI model that demonstrates analysing 1-lead ECG data to enable a variety of heart applications, such as detecting heart arrhythmias and capturing heart rate variability metrics. In addition, by analysing individual beats, the model can identify irregular beats, such as premature and ectopic beats originating in the atrium or ventricles.
"Ambiq's HeartKit may be the only open-source TinyML implementation of AI-based heart monitoring for IoT endpoint devices," said Carlos Morales, the VP of AI at Ambiq. "The highly optimised AI model will help developers enable health-tech applications on Ambiq Apollo4 Plus SoC in a matter of minutes."
By leveraging a modern multi-head network architecture coupled with Ambiq's low-power SoC, the model has been designed to be efficient, explainable, and extensible. While the pre-trained model is ready to use on Ambiq platforms, it also includes software to train, convert, and deploy customised models where needed.
The HeartKit has been released under the permissive BSD-3 license for ease of deployment and development.
