  Diagnosing pre-existing heart diseases early in life is important as it helps
prevent complications such as pulmonary hypertension, heart rhythm problems,
blood clots, heart failure and sudden cardiac arrest. To identify such
diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey
important information. Therefore, effectively using these two modalities of
data has the potential to improve the disease screening process. We evaluate
this hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which
contains simultaneously acquired PCG and ECG recordings. Our novel
Dual-Convolutional Neural Network based approach uses transfer learning to
tackle the problem of having limited amounts of simultaneous PCG and ECG data
that is publicly available, while having the potential to adapt to larger
datasets. In addition, we introduce two main evaluation frameworks named
record-wise and sample-wise evaluation which leads to a rich performance
evaluation for the transfer learning approach. Comparisons with methods which
used single or dual modality data show that our method can lead to better
performance. Furthermore, our results show that individually collected ECG or
PCG waveforms are able to provide transferable features which could effectively
help to make use of a limited number of synchronized PCG and ECG waveforms and
still achieve significant classification performance.
