  Objective: To determine if a realistic, but computationally efficient model
of the electrocardiogram can be used to pre-train a deep neural network (DNN)
with a wide range of morphologies and abnormalities specific to a given
condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress
Disorder, or PTSD - and significantly boost performance on a small database of
rare individuals.
  Approach: Using a previously validated artificial ECG model, we generated
180,000 artificial ECGs with or without significant TWA, with varying heart
rate, breathing rate, TWA amplitude, and ECG morphology. A DNN, trained on over
70,000 patients to classify 25 different rhythms, was modified the output layer
to a binary class (TWA or no-TWA, or equivalently, PTSD or no-PTSD), and
transfer learning was performed on the artificial ECG. In a final transfer
learning step, the DNN was trained and cross-validated on ECG from 12 PTSD and
24 controls for all combinations of using the three databases.
  Main results: The best performing approach (AUROC = 0.77, Accuracy = 0.72,
F1-score = 0.64) was found by performing both transfer learning steps, using
the pre-trained arrhythmia DNN, the artificial data and the real PTSD-related
ECG data. Removing the artificial data from training led to the largest drop in
performance. Removing the arrhythmia data from training provided a modest, but
significant, drop in performance. The final model showed no significant drop in
performance on the artificial data, indicating no overfitting.
  Significance: In healthcare, it is common to only have a small collection of
high-quality data and labels, or a larger database with much lower quality (and
less relevant) labels. The paradigm presented here, involving model-based
performance boosting, provides a solution through transfer learning on a large
realistic artificial database, and a partially relevant real database.
