Co-Author(s):
Philip Currie, MBBS - Cardiovascular Services
David Playford, MBBS FRACP PhD FCSANZ FESC FACCC - Professor, The
University of Notre Dame Australia
Luke Bollam, BEng - Alerte Digital Health
Razali Mohamad, MEng, BCM - Alerte Digital Health
Kushwin Rajamani, MBBS
Rukshen Weerasooriya, MBBS
universite de Bordeaux
Place de la Victoire, 33000 Bordeaux, France
Introduction | Objectives: Implanted loop recorders (ILR) have an important role to play in the
detection of atrial fibrillation (AF). Continuous home monitoring of
data from ILR may result in false positive AF detections due to a number
of factors including noisy signal and frequent ectopic beats. A pilot
study was designed to evaluate the use of AI to improve AF detection
from Biotronik Biomonitor 3 (BM3) recordings using a source agnostic AI
strategy.
Methods: A total of 1015 individual BM3 recordings were used in this study. The
BM3 data was uploaded to an ECG AI web application and was then manually
labelled by cardiologists. An AI model was created based upon labelled
data from a clinical database of 18,000 beat and rhythm labelled 12-lead
ECGs representing a wide range of arrhythmias and beat disorders. For
the purpose of this pilot study, the following minimal arrhythmia
labelling criteria were pre-defined: High relevance (eg atrial
fibrillation), Low relevance (eg sinus rhythm plus ectopics), Not
appraisable (eg noise). The following experiments (Exp) were conducted:
Exp 1. An AI model was created according to the minimal labelling
criteria only using 12-lead ECG data; Exp 2. The existing AI model was
assessed on BM3 data with no training using BM3 data; Exp 3. The
existing AI model was fine tuned using BM3 data under the following 2
scenarios: Exp 3a. 29% of BM3 data (295 recordings) used for fine
tuning and evaluated using 71% of BM3 data (717 recordings). Exp 3b
68% of BM3 data (688 recordings) used for fine tuning and evaluated
using 32% of BM3 data (327 recordings).
Results:
Exp 1 AUC 0.975004