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.

通过开发创新的多平台系统，实现基于人工智能的心电图解读解决方案的快速部署，并通过迅速的部署将人工智能的发现转化为对可穿戴式及便携式心电图设备的临床影响。

CarDS-Plus ECG 平台：便携和可穿戴设备心电图的多平台人工智能工具包的开发和可行性评估

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