  Myocardial Infarction (MI) has the highest mortality of all cardiovascular
diseases (CVDs). Detection of MI and information regarding its occurrence-time
in particular, would enable timely interventions that may improve patient
outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram
(ECG) recordings are currently used to screen MI patients. However, manual
inspection of ECGs is time-consuming and prone to subjective bias. Machine
learning methods have been adopted for automated ECG diagnosis, but most
approaches require extraction of ECG beats or consider leads independently of
one another. We propose an end-to-end deep learning approach, DeepMI, to
classify MI from normal cases as well as identifying the time-occurrence of MI
(defined as acute, recent and old), using a collection of fusion strategies on
12 ECG leads at data-, feature-, and decision-level. In order to minimise
computational overhead, we employ transfer learning using existing computer
vision networks. Moreover, we use recurrent neural networks to encode the
longitudinal information inherent in ECGs. We validated DeepMI on a dataset
collected from 17,381 patients, in which over 323,000 samples were extracted
per ECG lead. We were able to classify normal cases as well as acute, recent
and old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%,
respectively. We have demonstrated a multi-lead fusion approach to detect the
presence and occurrence-time of MI. Our end-to-end framework provides
flexibility for different levels of multi-lead ECG fusion and performs feature
extraction via transfer learning.
