The current study was performed to compare the automated system described above, against simultaneous ECG monitoring in a coronary care unit using a standard patient monitoring system. ECGs were de-identi�ed and randomized, and blinded R-R interval measurements were performed using manual digital calipers by three independent cardiologists.

METHOD RESULTECG acquisition and continuous wireless transmission showed excellent data integrity and no signi�cant dropouts. Storage and encryption of data was robust. The ECG signals acquired by our device appeared visually identical to the standard monitoring system. Agreement of R-R interval measurement between devices and observers was good, with RMSE and Bias at 26 and 6 ms, respectively. The rhythm comparison showed an accuracy of 93%.

CONCLUSION

BACKGROUNDCurrent non-invasive ECG devices are limited by duration of monitoring, size and weight of the device, discomfort, lack of real time transfer and lack of accurate automation.

Our goal was to produce long term, continuous real-time ECG monitoring for arrhythmia detection.

We have developed a framework that involves three independent but linked methodologies:

1. ECG acquisition on a small wearable device

2. Continuous wireless digital transfer of ECG data to a mobile device

3. Real-time arrhythmia-detection using arti�cial intelligence (AI).

To validate the ECG signal acquired by the Alerte continuous ECG acquisition device, by comparing simultaneously the Alerte device with a commercially available critical care ECG monitoring system.

AIM

Enabling the world’s first mobile embedded real-time source agnostic ecg AI

monitoring system

Our continuous, wearable ECG device with real time wireless data transmission to a mobile device provides robust data integrity, and good agreement compared with a standard ECG monitoring system. The framework provides a suitable platform for automated continuous arrhythmia detection on a smart phone, using AI.

ADDITIONAL INFORMATIONUsing a trained arti�cial intelligence model, we have been able to correctly classify between atrial �brillation and sinus rhythm. Please refer to our separate communication on validation of our AI models: A Validation Study of Automated Atrial Fibrillation Detection using Alerte Digital Health’s Arti�cial Intelligence System

AlerteDevice

GE Dash 5000Monitor

With matched signal data, the Alerte device proves itself to be highly reliable. The device enables the use ofArti�cial Intelligence to be able to detect arrythmias and predict future outcomes.

Epiphany ECGManagement System

Alerte Server

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Real-Time Source-Agnostic ECG Monitoringfor the use of Artificial Intelligence

David Playford, Razali Mohamad, Marcus Turewicz, Luke Bollam, Scott Martyn, Rukshen Weerasooriya, Pierre Jais

Alerte Digital Health 2017 - www.alertedh.com