CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms

TL;DR

This paper presents CarDS-Plus, a multiplatform AI toolkit for rapid, real-time interpretation of portable ECG data from wearable devices, enabling efficient clinical use and research integration.

Contribution

It introduces a novel multiplatform system that streamlines AI-based ECG analysis from various wearable devices, enhancing clinical deployment and research capabilities.

Findings

Mean reporting time of 33-36 seconds post-acquisition

No significant difference between Apple Watch and KardiaMobile in processing time

Successful integration of AI interpretation across multiple platforms

Abstract

In the rapidly evolving landscape of modern healthcare, the integration of wearable & portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams. Amidst the variety of data collected by these gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. There has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of rare cardiac disorders. This design study describes the development of an innovative multiplatform system aimed at the rapid deployment of AI-based ECG solutions for clinical investigation & care delivery. The study examines design considerations, aligning them with specific applications, develops data flows to maximize efficiency for research & clinical use. This process encompasses the reception of single-lead ECGs from diverse wearable devices, channeling this data into a centralized data lake & facilitating real-time inference through AI models for ECG interpretation. An evaluation of the platform demonstrates a mean duration from acquisition to reporting of results of 33.0 to 35.7 seconds, after a standard 30 second acquisition. There were no substantial differences in acquisition to reporting across two commercially available devices (Apple Watch and KardiaMobile). These results demonstrate the succcessful translation of design principles into a fully integrated & efficient strategy for leveraging 1-lead ECGs across platforms & interpretation by AI-ECG algorithms. Such a platform is critical to translating AI discoveries for wearable and portable ECG devices to clinical impact through rapid deployment.