AI-ECG AF Model
To tackle the complex problem of screening for AF, especially pertaining
to paroxysmal and SCAF, our group developed a unique model to predict
the likelihood of a person having underlying hidden AF from a sinus
rhythm ‘apparently normal’ ECG without any additional
information.11 The rationale for this study was that
mechanical remodeling in the form of myocyte hypertrophy, fibrosis and
chamber enlargement might lead to ECG changes yet undefined and too
subtle to be studied effectively by human potential. For instance,
evidence of interatrial block (Bayés syndrome) which is seldom reported
on ECGs correlates to both risk of incident AF and
stroke.50,51
Over 20 years of ECG data from 180,922 patients and 649,931 normal sinus
rhythm ECGs were analyzed. Patients were randomly assigned into 3
groups- 70% for training dataset, 10% for internal validation
(optimization and selecting hyperparameters) and 20% testing set
(previously unexposed ECGs). Within each dataset there were 2 groups-
patients with at least one ECG confirmed AF/atrial flutter diagnosis and
patients with no AF rhythms recorded. A 31-day window period preceding
the AF recorded ECG was taken in the disease group and all sinus rhythm
ECGs were included from the control group. This short 31-day period
prior to AF diagnosis was taken to include ECGs with the maximum
potential markers associated with AF and left atrial remodeling.
To optimize performance, eight independent leads (leads I, II, and
V1–6) were selected because any linear function of the leads could be
learned by the models (8×5000 matrix). The model was then tested on a
dataset of 130,802 sinus rhythm ECGs (3051 verified AF cases) (Table 1).
Model performed well with an AUC of 0·87 when a single ECG was used with
no additional information and an AUC of 0.90 when multiple ECGs were
used.